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1
Mahida, Ankur. "Predictive Incident Management Using Machine Learning." International Journal of Science and Research (IJSR) 11, no. 6 (2022): 1977–80. http://dx.doi.org/10.21275/sr24401231847.
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Tong, Tingting, and Zhen Li. "Predicting learning achievement using ensemble learning with result explanation." PLOS ONE 20, no. 1 (2025): e0312124. https://doi.org/10.1371/journal.pone.0312124.
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Predicting learning achievement is a crucial strategy to address high dropout rates. However, existing prediction models often exhibit biases, limiting their accuracy. Moreover, the lack of interpretability in current machine learning methods restricts their practical application in education. To overcome these challenges, this research combines the strengths of various machine learning algorithms to design a robust model that performs well across multiple metrics, and uses interpretability analysis to elucidate the prediction results. This study introduces a predictive framework for learning achievement based on ensemble learning techniques. Specifically, six distinct machine learning models are utilized to establish a base learner, with logistic regression serving as the meta learner to construct an ensemble model for predicting learning achievement. The SHapley Additive exPlanation (SHAP) model is then employed to explain the prediction results. Through the experiments on XuetangX dataset, the effectiveness of the proposed model is verified. The proposed model outperforms traditional machine learning and deep learning model in terms of prediction accuracy. The results demonstrate that the ensemble learning-based predictive framework significantly outperforms traditional machine learning methods. Through feature importance analysis, the SHAP method enhances model interpretability and improves the reliability of the prediction results, enabling more personalized interventions to support students.
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Ibrahim Adedeji Adeniran, Christianah Pelumi Efunniyi, Olajide Soji Osundare, and Angela Omozele Abhulimen. "Implementing machine learning techniques for customer retention and churn prediction in telecommunications." Computer Science & IT Research Journal 5, no. 8 (2024): 2011–25. http://dx.doi.org/10.51594/csitrj.v5i8.1489.
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This review paper explores the application of machine learning techniques in predicting customer churn and enhancing customer retention within the telecommunications industry. The paper begins by discussing the significance of customer churn, its causes, and the limitations of traditional churn prediction methods. It then delves into machine learning algorithms, including decision trees, support vector machines, and ensemble methods. It highlights their effectiveness in handling large and complex datasets typical of the telecom sector. The discussion extends to the challenges faced in data quality, model selection, implementation, and ethical considerations in using customer data for predictive analytics. The paper also compares machine learning models with traditional methods, emphasizing the advantages of scalability, accuracy, and real-time processing. Furthermore, it identifies potential innovations, such as improved data integration, interpretable models, and personalized retention strategies. Finally, the paper reflects on future trends, predicting the growing role of AI and machine learning in telecommunications, particularly in customer service automation and network optimization. The review underscores the importance of adopting machine learning to reduce churn and improve customer retention while considering the field's ethical implications and future opportunities. Keywords: Customer Churn Prediction, Machine Learning, Telecommunications, Customer Retention, Predictive Analytics, AI in Telecom
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Jogalekar, Tanmay. "Predictive Modelling for Stock Market Analysis (June 2025)." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem51062.
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The stock market is a complex and dynamic system, and predicting its behaviour is a challenging task. This research paper presents a comparative study of machine learning algorithms for predictive modelling of stock market analysis. We evaluate the performance of six machine learning algorithms, including Linear Regression, Decision Trees, Random Forest, Support Vector Machines, Artificial Neural Networks, and Gradient Boosting, on a dataset of historical stock prices. Our results show that the Gradient Boosting algorithm outperforms the other algorithms in terms of accuracy, precision, and recall. We also analyse the impact of feature engineering and hyperparameter tuning on the performance of the algorithms. The findings of this study can be used to develop predictive models for stock market analysis, which can aid investors and financial analysts in making informed decisions. Keywords - Stock Market Prediction, Predictive Modelling, Machine Learning, Deep Learning, LSTM, Financial Forecasting
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Husam Kadhim Gharkan, Mustafa Jawad Radif. "Predicting Student Performance Using a Hybrid Model Based on Machine Learning and Feature Selection Techniques." Journal of Information Systems Engineering and Management 10, no. 4 (2025): 192–99. https://doi.org/10.52783/jisem.v10i4.8921.
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Accurately predicting student performance plays a critical role in modern educational institutions. It enables targeted interventions and enhances educational outcomes. This paper proposes a hybrid predictive model for predicting student performance employing feature selection based on standard deviation filtering, coupled with machine learning techniques. In the machine learning phase used Decision Tree (DT), Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Machines (SVM) were used. The proposed model is tested and evaluated over the Student Performance Prediction—Multiclass Case dataset. The experimental result demonstrated robust predictive capabilities, with Decision Tree models showing the highest accuracy at 100%. KNN and Naive Bayes (NB) also exhibited strong performances, achieving accuracy rates of 98.98% and 96.94%, respectively. This work underscores the importance of selecting appropriate features and machine learning algorithms to optimise student performance prediction, significantly benefiting early identification of at-risk students.
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Nagarjuna, N., and Dr Lakshmi HN. "Predictive Modeling of Diabetes Mellitus Utilizing Machine Learning Techniques." CVR Journal of Science and Technology 26, no. 1 (2024): 112–17. http://dx.doi.org/10.32377/cvrjst2618.
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Diabetes mellitus represents a persistent metabolic condition distinguished by elevated levels of blood sugar, which results from the inadequacy of the body to secrete and respond to insulin, leading to health risks and frequent hospitalizations. Accurate predictive models are vital for targeted interventions to reduce readmissions and improve healthcare quality and cost. Early prediction can mitigate its impact, aid in control, and potentially save lives. Machine learning algorithms show promise in medical applications, including diabetes prediction and diagnosis. Limited data quality hinders accurate diabetes prediction due to missing values and inconsistencies. This paper investigates machine learning's potential for predicting and diagnosing diabetes, aiming to enhance accuracy and efficiency in disease management. Feature engineering techniques are applied to preprocess the data and extract relevant features for model development. To address class imbalance, SMOTE (Synthetic Minority Oversampling Technique) is employed. Various machine learning algorithms, including logistic regression, Naïve Bayes, random forests, support vector machines (SVM), K-Nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGBoost), are utilized to build predictive models. The performance evaluation employs standard metrics such as accuracy, recall, precision, and F1-Score. Notably, Random Forest achieves an accuracy of 82% followed by XGBoost(80%) , surpassing other ML algorithms utilized. Index Terms: Diabetes mellitus, Machine learning, Prediction, SVM, logistic regression, Accuracy.
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Natanael, David, and Hadi Sutanto. "Machine Learning Application Using Cost-Effective Components for Predictive Maintenance in Industry: A Tube Filling Machine Case Study." Journal of Manufacturing and Materials Processing 6, no. 5 (2022): 108. http://dx.doi.org/10.3390/jmmp6050108.
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Maintenance is an activity that cannot be separated from the context of product manufacturing. It is carried out to maintain the components’ or machines’ function so that no failure can reduce the machine’s productivity. One type of maintenance that can mitigate total machine failure is predictive maintenance. Predictive maintenance, along with the times, no longer relies on visuals or other senses but can be combined into automated observations using machine learning methods. It can be applied to a toothpaste factory with a tube filling machine by combining the results of sensor observations with machine learning methods. This research aims to increase the Overall equipment effectiveness (OEE) to 10% by predicting the components that will be damaged. The machine learning methods tested in this study are random forest regression and linear regression. This study indicates that the prediction accuracy of machine learning with the random forest regression method for PHM predictive is 88%of the actual data, and linear regression has an accuracy of 59% of the actual data. After implementing the system on the machine for three months, the OEE value increased by 13.10%, and unplanned machine failure decreased by 62.38% in the observed part. Implementation of the system can significantly reduce the failure factor of unplanned machines.
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MISHRA,, SAURABH. "HEALTH PREDICTION USING MACHINE LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34438.
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Machine learning techniques have transformed healthcare by enabling precise and timely disease prediction. The capacity to forecast multiple diseases simultaneously can greatly enhance early diagnosis and treatment, leading to improved patient outcomes and lower healthcare expenses. This research paper delves into the use of machine learning algorithms for predicting various diseases, highlighting their advantages, challenges, and prospects. It provides a comprehensive overview of different machine learning models and the data sources frequently employed in disease prediction. Furthermore, it emphasises the importance of feature selection, model evaluation, and the integration of diverse data types to improve prediction accuracy. The findings underscore the significant potential of machine learning in predicting multiple diseases and its impact on public health. Specifically, the study demonstrates the application of a machine learning model to determine if an individual is affected by certain diseases. This model is trained using sample data to enhance its predictive capabilities. Key Words: Disease Prediction, Disease data, Machine Learning.
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Sreshta, Maradapu Ananya. "Analyzing Cancer Prognosis with Advanced Machine Learning Models." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30443.
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This study investigates the use of Support Vector Machines (SVM) and other machine learning algorithms for predicting the prognosis of lung, breast, and cervical cancer patients. The research evaluates the predictive accuracy, influential features, algorithm performance, and model generalization across different cancer types. Using publicly available datasets, the study highlights SVM's exceptional accuracy in prognosis prediction, with findings indicating its superiority over alternative algorithms. Notably, it identifies the key clinical, molecular, and pathological features that significantly impact predictive accuracy. The study also discusses the clinical applicability of these models, emphasizing their potential to aid healthcare professionals in making more informed treatment decisions. Acknowledging limitations, including data availability and computational resources, the study suggests future directions, encouraging the exploration of additional techniques, diverse datasets, and real-world clinical trials to validate the model’s effectiveness. Keywords Cancer, Medical Diagnosis, Markers, Learning, Patients, Machine Learning, Support Vector Machine
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Fokkema, Marjolein, Dragos Iliescu, Samuel Greiff, and Matthias Ziegler. "Machine Learning and Prediction in Psychological Assessment." European Journal of Psychological Assessment 38, no. 3 (2022): 165–75. http://dx.doi.org/10.1027/1015-5759/a000714.
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Abstract. Modern prediction methods from machine learning (ML) and artificial intelligence (AI) are becoming increasingly popular, also in the field of psychological assessment. These methods provide unprecedented flexibility for modeling large numbers of predictor variables and non-linear associations between predictors and responses. In this paper, we aim to look at what these methods may contribute to the assessment of criterion validity and their possible drawbacks. We apply a range of modern statistical prediction methods to a dataset for predicting the university major completed, based on the subscales and items of a scale for vocational preferences. The results indicate that logistic regression combined with regularization performs strikingly well already in terms of predictive accuracy. More sophisticated techniques for incorporating non-linearities can further contribute to predictive accuracy and validity, but often marginally.
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Patil, Ketan. "Customer Churn Prediction Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29907.
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Customer churn, the phenomenon where customers cease their relationship with a business, is a critical concern for e-commerce platforms striving for sustained growth and profitability. Predicting churn in advance can empower businesses to implement proactive retention strategies, thereby mitigating revenue loss and enhancing customer satisfaction. In this study, we propose a machine learning-based approach to predict customer churn in e-commerce settings. We begin by collecting extensive data encompassing various customer attributes, transactional history, browsing behavior, and engagement metrics. Leveraging this rich dataset, we employ state- of-the-art machine learning algorithms such as logistic regression, random forests, gradient boosting machines, and neural networks for predictive modeling. Feature engineering techniques are applied to extract meaningful patterns and insights from the raw data, enhancing the predictive performance of the models. By deploying the developed predictive model into production environments, businesses can proactively identify at-risk customers and tailor targeted retention strategies to mitigate churn. The results demonstrate the effectiveness of machine learning in accurately predicting customer churn in e-commerce, enabling businesses to proactively implement retention strategies and enhance customer engagement. Index Terms— Machine learning, E-commerce, Logistic re- gression, Decision tree, Random forest algorithm.
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Yadav, Shivani. "Heart Disease Prediction Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–14. http://dx.doi.org/10.55041/ijsrem36858.
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Heart disease remains a leading cause of mortality worldwide, necessitating improved methods for early detection and risk assessment. This paper reviews and analyzes the application of machine learning techniques in heart disease prediction, focusing on five primary algorithms: Naïve Bayes, k-Nearest Neighbor (KNN), Decision Tree, Artificial Neural Network (ANN), and Random Forest. By examining existing studies and datasets, we evaluate the effectiveness of these algorithms in predicting heart disease risk. Our analysis demonstrates that machine learning models can significantly enhance the accuracy and efficiency of heart disease prediction compared to traditional diagnostic methods. The Random Forest algorithm exhibited the highest overall performance, with studies reporting accuracy rates up to 95% in identifying potential heart disease cases. This review highlights the potential of machine learning in revolutionizing cardiovascular healthcare by enabling more personalized risk assessments and facilitating early intervention strategies. The integration of these advanced predictive models into clinical practice could substantially improve patient outcomes and reduce the global burden of heart disease. Keywords: Cardiovascular Risk Prediction, Machine Learning Algorithms, Electronic Health Records, Random Forest, Artificial Neural Networks, Feature Importance, Clinical Decision Support, Personalized Medicine, Predictive Analytics in Healthcare, Early Disease Detection.
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Journal, IJSREM. "PARKINSON’S DISEASE PREDICTION USING MACHINE LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–13. http://dx.doi.org/10.55041/ijsrem27762.
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Parkinson's disease (PD) is a condition that affects a substantial number of individuals worldwide, leading to impairments in motor functions and a decline in overall quality of life. The timely identification of PD is vital for effective intervention and better patient outcomes. This study investigates the Utilization of machine learning methods in predicting and diagnosing Parkinson's disease using clinical and biomedical data.The dataset is pre-processed to address missing values and standardize features. Subsequently, various ML algorithms, including Support Vector Machines (SVM) are employed to develop predictive models.The research evaluates the performance of each model through rigorous training and testing procedures, utilizing metrics such as accuracy, sensitivity, and specificity. Feature importance analysis is conducted to identify key factors contributing to the predictive accuracy of the models. Additionally, the study investigates the impact of different feature subsets on model performance.The proposed ML models exhibit promising results in accurately predicting Parkinson's disease based on diverse sets of features. The research contributes to the ongoing efforts to develop non-invasive and efficient diagnostic tools for Parkinson's disease, providing a foundation for further studies and potential integration into clinical practice. A linear kernel utilized in a support vector machine classifier trained on a dataset with diverse voice-related features, demonstrated exceptional performance in predicting Parkinson's disease. The user interface was designed with a focus on availability, make secure that users can easily navigate and understand the application. The prediction result, either An individual has been Received a diagnosis of Parkinson's disease or The person does not have Parkinson's disease," is displayed. The model achieved an accuracy of (87%) on the test setThe high training accuracy of (86%) further affirms the model's capability to capture underlying patterns in the training data. Keywords: Parkinson's disease, machine learning, predictive modelling, feature importance, neurodegenerative disorders.
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Ali, Ahmed, Ahmed Fathalla, Ahmad Salah, Mahmoud Bekhit, and Esraa Eldesouky. "Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models." Computational Intelligence and Neuroscience 2021 (November 27, 2021): 1–13. http://dx.doi.org/10.1155/2021/8551167.
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Nowadays, ocean observation technology continues to progress, resulting in a huge increase in marine data volume and dimensionality. This volume of data provides a golden opportunity to train predictive models, as the more the data is, the better the predictive model is. Predicting marine data such as sea surface temperature (SST) and Significant Wave Height (SWH) is a vital task in a variety of disciplines, including marine activities, deep-sea, and marine biodiversity monitoring. The literature has efforts to forecast such marine data; these efforts can be classified into three classes: machine learning, deep learning, and statistical predictive models. To the best of the authors’ knowledge, no study compared the performance of these three approaches on a real dataset. This paper focuses on the prediction of two critical marine features: the SST and SWH. In this work, we proposed implementing statistical, deep learning, and machine learning models for predicting the SST and SWH on a real dataset obtained from the Korea Hydrographic and Oceanographic Agency. Then, we proposed comparing these three predictive approaches on four different evaluation metrics. Experimental results have revealed that the deep learning model slightly outperformed the machine learning models for overall performance, and both of these approaches greatly outperformed the statistical predictive model.
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Alam, Gazi Touhidul, Mohammed Majid Bakhsh, Nusrat Yasmin Nadia, and S. A. Mohaiminul Islam. "Predictive Analytics in QA Automation:." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 4, no. 2 (2025): 55–66. https://doi.org/10.60087/jklst.v4.n2.005.
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An essential component of contemporary software development is quality assurance (QA) automation, which guarantees program dependability, effectiveness, and user pleasure. Traditional QA techniques, on the other hand, frequently have trouble finding flaws early in the software development lifecycle, which raises expenses and delays releases. By predicting possible flaws before they appear, predictive analytics which is fueled by machine learning (ML) and artificial intelligence (AI) offers a revolutionary approach to QA automation. This study examines how predictive analytics might improve software quality and expedite testing procedures, hence redefining defect prevention for American businesses. This study uses a systematic methodology that combines machine learning-based defect prediction with real-world case studies, analyzing defect trends and evaluating the effectiveness of predictive models. The results show that enterprises leveraging predictive analytics in QA automation experience higher defect detection rates reduced testing overhead, and faster release cycles. The study identifies key machine learning models, such as Random Forests, Support Vector Machines (SVM), and Neural Networks, which have demonstrated significant accuracy in defect prediction. It also discusses the integration of predictive analytics within DevOps and CI/CD pipelines, enabling continuous monitoring and proactive defect prevention. Defect prediction skills will be significantly improved in the future by developments in Explainable AI (XAI), deep learning models, and Natural Language Processing (NLP). In addition to supporting data-driven decision-making, model transparency, and continuous learning frameworks, this article offers important advice for businesses looking to integrate predictive analytics into their QA procedures. U.S. businesses may go from reactive to proactive QA approaches by adopting predictive analytics, which will guarantee better software quality, lower expenses, and an enhanced user experience
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Sakalle, Sonal, and Sakshi Rai. "Effectiveness of a machine learning classifier model." Journal of Advances and Scholarly Researches in Allied Education 22, no. 01 (2025): 34–43. https://doi.org/10.29070/4sfq6836.
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As a statistical tool, predictive modelling may foretell how something will act in the future. In order to foretell how people will act in the future, machine learning has become a popular tool. Determining which of the many accessible algorithms is best suited to the data at hand is an intriguing challenge. The field of study that finds the greatest value in predictive modelling is educational data mining. Accurately predicting undergraduate students' grades has several benefits for both students and teachers. Students might be more motivated to choose their future endeavours with the support of early prediction. Using data gathered from undergraduate studies, this study displays the outcomes of many machine learning methods. It uses data obtained from undergraduate studies to assess the efficacy of several machine learning methods. Choosing the proper characteristics and the right prediction algorithm are two big problems that are addressed.
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Jhajharia, Kavita, Neha V. Sharma, and Pratistha Mathur. "A Machine Learning Model for Crop Yield Prediction Using Remote Sensing Data." International Research Journal of Multidisciplinary Scope 06, no. 02 (2025): 577–90. https://doi.org/10.47857/irjms.2025.v06i02.03182.
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Precisely estimating crop yields is a critical aspect of agricultural planning, resource allocation, and food security. Satellite data integrated with machine learning algorithms have recently become a potential solution for predicting crop yield at local and global levels. The present study provides detailed investigation of satellite-based crop yield prediction using machine-learning algorithms. The proposed methodology integrates satellite imagery data with precipitation data. We use machine learning algorithms for predictive modelling, random forests, support vector machines, decision trees, linear regression, and k-nearest neighbour. Extensive investigations are conducted to examine the effectiveness of the proposed method. The study employs multi-year satellite imagery and corresponding crop yield data from various agricultural regions to develop predictive models. The models are trained and tested while considering temporal and spatial variations. Model accuracy and reliability are evaluated through performance metrics, including mean absolute error and root mean square error. The study’s findings indicate that using machine learning algorithms for satellite-based crop yield prediction yields a significant level of accuracy compared with standard techniques. According to the research conducted, it has been found that among all the methods that were implemented, the support vector machine method has shown better performance. Integrating satellite-based techniques and machine learning algorithms presents a viable and scalable approach to predicting crop yields.
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M, Mrs Adithi, ,. Pavan Kumar R, Priya Y. S, Sneha B. S, and Vaishnavi O. "Smart Healthcare Prediction Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/ijsrem39440.
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In this paper, the utilization of machine learning techniques in the healthcare system is introduced. As the healthcare industry generates increasingly vast amounts of data daily, manual processing by humans becomes impractical for prompt disease diagnosis and treatment decisions. To address this challenge, data management techniques and machine learning algorithms are explored in healthcare applications to facilitate more accurate decision-making processes. Detailed descriptions of medical data are provided, enhancing various facets of healthcare applications through the adoption of this cutting-edge technology. Naïve Bayes machine learning algorithm is employed to train the machine for predicting various diseases, extracting new patterns from extensive datasets to enable predictive analysis and derive knowledge linked to these patterns. A key focus lies in acquiring data automatically or semi-automatically, highlighting the significance of this process. Key Words: Machine Learning, Healthcare, Naïve Bayes Algorithm, Predictive Models, Data prediction.
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Basha, Md Chan, A. Veena sindhu, CH .Vaishnavi, E. Sujatha, and K. Praveen kumar. "Lung Cancer Prediction using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–9. https://doi.org/10.55041/ijsrem40415.
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Past years have experienced increasing mortality rate due to lung cancer and thus it become crucial to predict whether the tumor has transformed to cancer or not, if the prediction is made at an early stage then many lives can be saved. This investigates the potential of machine learning (ML) algorithms for predicting lung cancer risk based on clinical data and Medical Imaging, patient demographics and several ML models, including Decision Trees, Support Vector Machines (SVM), random forests and K- Nearest neighbors(KNN),CNN(convolutional neural network).Evaluation metrics like accuracy ,precision, recall, were used to assess model effectiveness and based on classification results obtained. Prediction is made whether the tumor is begin or malignant. The inevitable parameters such as accuracy, recall, and precision are calculated for determining which algorithm has the highest predictive accuracy. The findings suggest that machine learning can play a pivotal role in identifying high-risk patients early, facilitating timely intervention and improving outcome. Key Words: Support Vector Machines (SVM), k-Nearest Neighbors KNN), Patient data
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Alassafi, Madini O., Wajdi Alghamdi, S. Sathiya Naveena, Ahmed Alkhayyat, Absalomov Tolib, and Ibrokhimov Sarvar Muydinjon Ugli. "Machine Learning for Predictive Analytics in Social Media Data." E3S Web of Conferences 399 (2023): 04046. http://dx.doi.org/10.1051/e3sconf/202339904046.
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Machine Learning (ML) has become a potent predictive analytics tool in several fields, including the study of social media data. Social media sites have developed into massive repositories of user-generated information, providing insightful data about user trends, interests, and behavior. This abstract emphasizes the use of machine learning methods for predictive analytics in social media data and examines the potential and problems unique to this field. Utilizing the capabilities of machine learning algorithms to identify significant trends and forecast user behavior from social media data is the goal of this study. The study makes use of a sizable dataset made up of user profiles, blog posts, comments, and engagement metrics gathered from well-known social networking sites. Predictive models are created using a variety of machine learning algorithms, such as ensemble methods, neural networks, decision trees, and support vector machines. As a result, this study emphasizes how important machine learning is for doing predictive analytics on social media data. The employment of diverse algorithms and preprocessing methods yields insightful information about user behavior and enables precise prediction of user behaviors. To improve the prediction powers of machine learning in this area, future research should concentrate on tackling the obstacles related to social media data, such as privacy concerns and data quality issues.
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J Nadhiya R and P. Rajendran. "Prediction of Machine Failure Status Using Machine Learning Techniques." International Journal of Scientific Research in Science and Technology 12, no. 4 (2025): 122–26. https://doi.org/10.32628/ijsrst251262.
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This abstract presents a study on predicting machine failure status using machine learning techniques. With the increasing complexity of industrial systems, early detection of machinery failures is crucial for maintaining operational efficiency and minimizing downtime. In this research, various machine learning algorithms are employed to analyse historical sensor data and identify patterns indicative of impending failures. The proposed approach demonstrates significant potential in accurately predicting machine failures, thus enabling proactive maintenance strategies. Experimental results showcase the effectiveness of the model in achieving high accuracy and precision in predicting failure conditions across diverse industrial settings. This work contributes to the field of predictive maintenance by harnessing the power of machine learning to enhance operational reliability and optimize maintenance schedules.Then Industrial equipment performance control and failure prediction are important not just for the quality of the produced material, but also for the amount of time and money saved in overall maintenance. This project aims to monitor the evolution of AI/ML techniques for equipment fault prediction in industries over time. The topics covered in this paper include machine learning algorithms, use cases, and principles related to the application of such technology in a variety of industries such as software and hardware.
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Yeo, Michelle, Tristan Fletcher, and John Shawe-Taylor. "Machine Learning in Fine Wine Price Prediction." Journal of Wine Economics 10, no. 2 (2015): 151–72. http://dx.doi.org/10.1017/jwe.2015.17.
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AbstractAdvanced machine learning techniques like Gaussian process regression and multi-task learning are novel in the area of wine price prediction; previous research in this area being restricted to parametric linear regression models when predicting wine prices. Using historical price data of the 100 wines in the Liv-Ex 100 index, the main contributions of this paper to the field are, firstly, a clustering of the wines into two distinct clusters based on autocorrelation. Secondly, an implementation of Gaussian process regression on these wines with predictive accuracy surpassing both the trivial and simple ARMA and GARCH time series prediction benchmarks. Lastly, an implementation of an algorithm which performs multi-task feature learning with kernels on the wine returns as an extension to our optimal Gaussian process regression model. Using the optimal covariance kernel from Gaussian process regression, we achieve predictive results which are comparable to that of Gaussian process regression. Altogether, our research suggests that there is potential in using advanced machine learning techniques in wine price prediction. (JEL Classifications: C6, G12)
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Gangthade, Ritesh Ananda. "Stock Price Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 3472–77. http://dx.doi.org/10.22214/ijraset.2024.60725.
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Abstract: The "Stock Price Prediction Using Machine Learning" project aims to develop an advanced predictive model for forecasting stock prices in financial markets. The volatility and complexity of stock markets make accurate predictions challenging, and the utilization of machine learning techniques offers a promising approach to address this challenge. This project leverages historical stock data, technical indicators, and sentiment analysis to create a robust predictive model. The methodology involves collecting and preprocessing a vast dataset of historical stock prices and relevant financial indicators. Various machine learning algorithms, including but not limited to linear regression, decision trees, support vector machines, and neural networks, are employed to analyze patterns and relationships within the data. The project focuses on model evaluation and comparison to identify the most accurate and reliable prediction model. Performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and accuracy are utilized to assess the effectiveness of the models. Hyperparameter tuning and cross- validation are employed to enhance the models' generalization capabilities
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Vijayakumar, Ranjith, and Mike W. L. Cheung. "Replicability of Machine Learning Models in the Social Sciences." Zeitschrift für Psychologie 226, no. 4 (2018): 259–73. http://dx.doi.org/10.1027/2151-2604/a000344.
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Abstract. Machine learning tools are increasingly used in social sciences and policy fields due to their increase in predictive accuracy. However, little research has been done on how well the models of machine learning methods replicate across samples. We compare machine learning methods with regression on the replicability of variable selection, along with predictive accuracy, using an empirical dataset as well as simulated data with additive, interaction, and non-linear squared terms added as predictors. Methods analyzed include support vector machines (SVM), random forests (RF), multivariate adaptive regression splines (MARS), and the regularized regression variants, least absolute shrinkage and selection operator (LASSO), and elastic net. In simulations with additive and linear interactions, machine learning methods performed similarly to regression in replicating predictors; they also performed mostly equal or below regression on measures of predictive accuracy. In simulations with square terms, machine learning methods SVM, RF, and MARS improved predictive accuracy and replicated predictors better than regression. Thus, in simulated datasets, the gap between machine learning methods and regression on predictive measures foreshadowed the gap in variable selection. In replications on the empirical dataset, however, improved prediction by machine learning methods was not accompanied by a visible improvement in replicability in variable selection. This disparity is explained by the overall explanatory power of the models. When predictors have small effects and noise predominates, improved global measures of prediction in a sample by machine learning methods may not lead to the robust selection of predictors; thus, in the presence of weak predictors and noise, regression remains a useful tool for model building and replication.
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Patel, Ananya (Ph D. Candidate). "ADVANCING FINANCIAL PREDICTION THROUGH QUANTUM MACHINE LEARNING." International Journal of Intelligent Data and Machine Learning 2, no. 02 (2025): 1–7. https://doi.org/10.55640/ijidml-v02i02-01.
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The growing complexity, interdependencies, and rapid fluctuations inherent in modern financial markets create substantial challenges for accurate forecasting, portfolio optimization, and risk management. Conventional machine learning techniques, while powerful, often face limitations in capturing nonlinear relationships and processing high-dimensional datasets efficiently. Quantum machine learning (QML) has emerged as a promising paradigm that leverages quantum computing principles to enhance predictive modeling in finance. This study presents a comprehensive investigation into the application of QML methods—including variational quantum circuits, quantum kernel estimation, and quantum-enhanced support vector machines—for financial time-series prediction and asset price classification. We propose a hybrid quantum-classical framework that integrates quantum feature mapping with classical optimizers to improve model expressiveness and convergence. Empirical experiments are conducted using historical stock market data and synthetic datasets to benchmark QML approaches against established classical models such as long short-term memory networks and gradient boosting machines. The results demonstrate that QML techniques can achieve superior prediction accuracy and lower computational latency under certain data regimes, particularly when dealing with small-to-medium-sized datasets and high feature correlations. Additionally, the study examines scalability considerations, hardware constraints of near-term quantum devices, and the interpretability of quantum model outputs in financial decision-making contexts. The findings underscore the transformative potential of quantum machine learning as an innovative tool for advancing predictive analytics in finance and provide practical insights into how financial institutions can begin integrating QML capabilities into their workflows.
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Xu, Hai, Jian Zhou, Panagiotis G. Asteris, Danial Jahed Armaghani, and Mahmood Md Tahir. "Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate." Applied Sciences 9, no. 18 (2019): 3715. http://dx.doi.org/10.3390/app9183715.
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Predicting the penetration rate is a complex and challenging task due to the interaction between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the use of empirical and theoretical techniques in predicting TBM performance. However, reliable performance prediction of TBM is of crucial importance to mining and civil projects as it can minimize the risks associated with capital costs. This study presents new applications of supervised machine learning techniques, i.e., k-nearest neighbor (KNN), chi-squared automatic interaction detection (CHAID), support vector machine (SVM), classification and regression trees (CART) and neural network (NN) in predicting the penetration rate (PR) of a TBM. To achieve this aim, an experimental database was set up, based on field observations and laboratory tests for a tunneling project in Malaysia. In the database, uniaxial compressive strength, Brazilian tensile strength, rock quality designation, weathering zone, thrust force, and revolution per minute were utilized as inputs to predict PR of TBM. Then, KNN, CHAID, SVM, CART, and NN predictive models were developed to select the best one. A simple ranking technique, as well as some performance indices, were calculated for each developed model. According to the obtained results, KNN received the highest-ranking value among all five predictive models and was selected as the best predictive model of this study. It can be concluded that KNN is able to provide high-performance capacity in predicting TBM PR. KNN model identified uniaxial compressive strength (0.2) as the most important and revolution per minutes (0.14) as the least important factor for predicting the TBM penetration rate.
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NANDHINI K, Ms. "SALES FORECASTING USING MACHINE LEARNING." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03473.
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Abstract - Sales forecasting remains a critical component in the strategic planning and operational efficiency of retail enterprises. This study presents a data-driven forecasting model tailored for retail sales prediction, using a real-world dataset comprising historical sales records, outlet characteristics, and product features. The methodology emphasizes robust data preprocessing, including handling of missing values, outlier treatment, and categorical encoding, followed by the application of ensemble-based regression. Among various algorithms evaluated, the Extreme Gradient Boosting Random Forest Regressor (XGBRFRegressor) demonstrated superior predictive performance, achieving consistent accuracy across cross-validation folds. To enhance practical applicability, the forecasting system was integrated into an interactive interface, enabling real-time prediction based on user-defined input parameters. The proposed approach offers a scalable and reliable framework for informed decision-making in retail operations, particularly in inventory management, demand planning, and revenue optimization. Key Words: predicting, forecasting, demand planning, sales
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Talele, Amit. "Customer Churn Prediction Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41260.
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Customer attrition poses a major issue for the telecommunications sector, resulting in revenue decline and higher expenses for gaining new customers. Predicting churn accurately enables telecom companies to take proactive measures to retain customers. This study leverages specifically Random Forest, Decision Tree, and XGBoost, to develop a robust churn prediction model. These algorithms analyze customer behavioral data, including call duration, internet usage, billing history, and complaints, to identify potential churners. Decision Tree provides an interpretable model, Random Forest improves predictive accuracy through ensemble learning, and XGBoost enhances performance with gradient boosting and optimized handling of imbalanced datasets. The proposed model assists telecom companies in classifying customers based on their churn risk, enabling the implementation of targeted retention approaches like tailored discounts and rewards programs. By integrating advanced machine learning techniques, telecom service providers can enhance customer retention, minimize the churn rates, and improves the business sustainability. The study highlights the importance of data-driven decision-making in the telecom sector, demonstrating how predictive analytics can optimize customer relationship management and drive profitability. Key Words: Predicting telecom customer churn, machine learning techniques, Random Forest algorithm, Decision Tree method, XGBoost model, retention of customers, analytics for forecasting, gradient boosting techniques, telecom sector.
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Gusev, I. V., D. V. Gavrilov, R. E. Novitsky, T. Yu Kuznetsova, and S. A. Boytsov. "Improvement of cardiovascular risk assessment using machine learning methods." Russian Journal of Cardiology 26, no. 12 (2021): 4618. http://dx.doi.org/10.15829/1560-4071-2021-4618.
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The increase in the prevalence of cardiovascular diseases (CVDs) specifies the importance of their prediction, the need for accurate risk stratification, preventive and treatment interventions. Large medical databases and technologies for their processing in the form of machine learning algorithms that have appeared in recent years have the potential to improve predictive accuracy and personalize treatment approaches to CVDs. The review examines the application of machine learning in predicting and identifying cardiovascular events. The role of this technology both in the calculation of total cardiovascular risk and in the prediction of individual diseases and events is discussed. We compared the predictive accuracy of current risk scores and various machine learning algorithms. The conditions for using machine learning and developing personalized tactics for managing patients with CVDs are analyzed.
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B.S, Mr Akshay Nayak. "Predictive Medicine Recommendation System Powered by Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50712.
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Abstract - The Predictive Medicine Recommendation System Powered by Machine learning (ML), a system where intelligently suggests suitable medications based on user-provided inputs, using machine learning techniques to enhance accuracy and relevance symptoms or medical conditions. By integrating advanced classification algorithms such as Support Vector Machines (SVM), Random Forest, and Naive Bayes, the system aims to reduce prescription errors, enhance diagnostic efficiency, and support personalized healthcare delivery. The platform supports early disease prediction, medication advice, and risk mitigation by analysing input from electronic health records (EHRs) and user-reported symptoms. Results demonstrate high accuracy in disease classification and medicine recommendation. This system represents a step forward in personalized, technology-enabled healthcare. Key words - Machine Learning, Disease Prediction, Medicine Recommendation, Personalized Healthcare.
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Shin, Seung-Jun, Young-Min Kim, and Prita Meilanitasari. "A Holonic-Based Self-Learning Mechanism for Energy-Predictive Planning in Machining Processes." Processes 7, no. 10 (2019): 739. http://dx.doi.org/10.3390/pr7100739.
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The present work proposes a holonic-based mechanism for self-learning factories based on a hybrid learning approach. The self-learning factory is a manufacturing system that gains predictive capability by machine self-learning, and thus automatically anticipates the performance results during the process planning phase through learning from past experience. The system mechanism, including a modeling method, architecture, and operational procedure, is structured to agentize machines and manufacturing objects under the paradigm of Holonic Manufacturing Systems. This mechanism allows machines and manufacturing objects to acquire their data and model interconnection and to perform model-driven autonomous and collaborative behaviors. The hybrid learning approach is designed to obtain predictive modeling ability in both data-existent and even data-absent environments via accommodating machine learning (which extracts knowledge from data) and transfer learning (which extracts knowledge from existing knowledge). The present work also implements a prototype system to demonstrate automatic predictive modeling and autonomous process planning for energy reduction in milling processes. The prototype generates energy-predictive models via hybrid learning and seeks the minimum energy-using machine tool through the contract net protocol combined with energy prediction. As a result, the prototype could achieve a reduction of 9.70% with respect to energy consumption as compared with the maximum energy-using machine tool.
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Rachakonda, Shriya Aishani, Srinidhi Pudipedi, and T. S. Shiny Angel. "PREDICTIVE MODELLING FOR DIABETES USING MACHINE LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 008 (2024): 1–16. http://dx.doi.org/10.55041/ijsrem37149.
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Diabetes is a prevalent and long-lasting medical disorder that has significant consequences for health. It is important to diagnose diabetes promptly and accurately in order to effectively manage it. This study use machine learning algorithms to forecast the occurrence of diabetes by analyzing a dataset obtained from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Important diagnostic characteristics such as the count of pregnancies, insulin levels, age, body mass index (BMI), and other health measurements are used. We utilize various supervised learning classification methods, including Logistic Regression, Support Vector Machines (SVM), Decision Trees, k-Nearest Neighbours (k- NN), and Random Forest, in order to create a reliable predictive model. The study entails thorough data preprocessing, meticulous feature selection, and rigorous model training to guarantee the precision and dependability of predictions. Performance indicators, such as accuracy, precision, recall, F1- score, and the Area Under the Receiver Operating Characteristic Curve (AUC), are employed to assess the efficacy of each algorithm. The objective of this research is to enhance the identification and treatment of diabetes at an early stage, hence enhancing the effectiveness of healthcare interventions. This effort aims to enhance predictive modelling in the field of diabetes using advanced machine learning techniques. Key Words: Diabetes Prediction, Machine Learning, Logistic Regression, Support Vector Machines, Decision Trees, k- Nearest Neighbours, Random Forest, Predictive Modelling, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
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Khaja, Abdullah Mazharuddin, Michidmaa Arikhad, Yawar Hayat, and Saad Rasool. "Predictive Modeling for Chemotherapy Response Using Machine Learning." International Journal of Innovative Research in Computer Science and Technology 13, no. 3 (2025): 62–66. https://doi.org/10.55524/ijircst.2025.13.3.10.
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One of the most difficult and urgent problems in oncology is still predicting how a patient will react to chemotherapy. Interpatient variability still restricts therapeutic success and increases the likelihood of side effects, even with major improvements in treatment regimens. Machine learning (ML) has been a game-changing technique in biomedical research in recent years, allowing high-dimensional information to be integrated and interpreted to inform clinical judgment. With an emphasis on both historical advancements and contemporary advances, this thesis offers a thorough analysis of the function of machine learning in predicting the results of chemotherapy. After examining the fundamental ideas and early applications of machine learning in oncology, we provide a thorough analysis of current supervised and unsupervised learning methods used in chemotherapy response prediction. Neural networks, random forests, support vector machines, and clustering algorithms are important techniques. The use of reputable public datasets as standards for model training and validation, including The Cancer Genome Atlas (TCGA), Genomics of Drug Sensitivity in Cancer (GDSC), and Cancer Cell Line Encyclopedia (CCLE), is also covered in the thesis.Particular focus is placed on real-world clinical application, model interpretability, and performance evaluation criteria. We also discuss data biases, generalizability issues, and ethical problems. Finally, by allowing for therapy customization based on unique genetic and molecular profiles, we investigate how these predictive models can hasten the shift to precision oncology.
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Vora, Deepali, and Kamatchi Iyer. "Evaluating the Effectiveness of Machine Learning Algorithms in Predictive Modelling." International Journal of Engineering & Technology 7, no. 3.4 (2018): 197. http://dx.doi.org/10.14419/ijet.v7i3.4.16773.
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Predictive modelling is a statistical technique to predict future behaviour. Machine learning is one of the most popular methods for predicting the future behaviour. From the plethora of algorithms available it is always interesting to find out which algorithm or technique is most suitable for data under consideration. Educational Data Mining is the area of research where predictive modelling is most useful. Predicting the grades of the undergraduate students accurately can help students as well as educators in many ways. Early prediction can help motivating students in better ways to select their future endeavour. This paper presents the results of various machine learning algorithms applied to the data collected from undergraduate studies. It evaluates the effectiveness of various machine learning algorithms when applied to data collected from undergraduate studies. Two major challenges are addressed as: choosing the right features and choosing the right algorithm for prediction.
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Swamy, Mr K. K., Mrs SRILATHA PULI, S. SWETHA, B. SHARANYA, A. ANUHYA, and J. SHREYAS. "CRIME ANALYSIS USING MACHINE LEARNING." YMER Digital 21, no. 05 (2022): 412–16. http://dx.doi.org/10.37896/ymer21.05/44.
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Crimes are the significant threat to the humankind. as crimes are increasing at a rapid rate approach for identifying trends in crime. Our project can predict regions which have high probability for crime occurrence and can visualize crime prone areas by visual representation of data by bar graphs, pie charts etc. It speed up the classification of criminal activities by calculating the average accuracy rate .It uses crime data set and predicts the types of crimes in a particular area which help in Accurate results based on the predictive analysis of logistic regression. The objective would be train a model for prediction using logistic regression classification algorithm. Logistic regression used for classification problems and it is a predictive analysis algorithm based on concept of probability. Crime analysis project is a systematic approach for identifying trends in crime. .It uses crime data set and predicts the types of crimes in a particular area which help in Accurate results based on the predictive analysis of logistic regression.
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Bussa, Santhosh. "Machine Learning in Predictive Quality Assurance." Stallion Journal for Multidisciplinary Associated Research Studies 1, no. 6 (2022): 54–66. https://doi.org/10.55544/sjmars.1.6.8.
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Predictive quality assurance (PQA) uses machine learning (ML) for enhancing the quality assurance process from traditional reactive systems to proactive predictive systems: predicting and preventing defects toward quality across the board. This paper is an exploration of the ML techniques of PQA with a focus on supervised, unsupervised, and reinforcement models, along with their interaction with real-time quality control systems. Techniques of data preprocessing, dealing with imbalanced datasets, and validation of the model in detail are discussed. Major applications in manufacturing, automotive, and electronic areas are described, together with ethical concerns and challenges. Future directions focus on self-governing quality assurance systems that are assisted by high AI algorithms.
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Li, Xiaochang, Xiaoman Chen, Qiulian Wang, Ning Yang, and Congjiao Sun. "Integrating Bioinformatics and Machine Learning for Genomic Prediction in Chickens." Genes 15, no. 6 (2024): 690. http://dx.doi.org/10.3390/genes15060690.
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Genomic prediction plays an increasingly important role in modern animal breeding, with predictive accuracy being a crucial aspect. The classical linear mixed model is gradually unable to accommodate the growing number of target traits and the increasingly intricate genetic regulatory patterns. Hence, novel approaches are necessary for future genomic prediction. In this study, we used an illumina 50K SNP chip to genotype 4190 egg-type female Rhode Island Red chickens. Machine learning (ML) and classical bioinformatics methods were integrated to fit genotypes with 10 economic traits in chickens. We evaluated the effectiveness of ML methods using Pearson correlation coefficients and the RMSE between predicted and actual phenotypic values and compared them with rrBLUP and BayesA. Our results indicated that ML algorithms exhibit significantly superior performance to rrBLUP and BayesA in predicting body weight and eggshell strength traits. Conversely, rrBLUP and BayesA demonstrated 2–58% higher predictive accuracy in predicting egg numbers. Additionally, the incorporation of suggestively significant SNPs obtained through the GWAS into the ML models resulted in an increase in the predictive accuracy of 0.1–27% across nearly all traits. These findings suggest the potential of combining classical bioinformatics methods with ML techniques to improve genomic prediction in the future.
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Li, Yanrong, Shibiao Xu, Li Guo, Qing Zhang, and Hailong Jin. "PREDICTION OF KIDNEY TRANSPLANTATION INFECTION BASED ON TRADITIONAL MACHINE LEARNING AND DEEP LEARNING." International Journal on Bioinformatics & Biosciences 13, no. 1 (2023): 1–15. http://dx.doi.org/10.5121/ijbb.2023.13102.
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Infection prediction after kidney transplantation is significant. Most existing models for predicting kidney transplant infection are statistical, unintelligent, and straightforward. The foremost task of this paper is to analyze kidney transplantation data, introduce existing traditional machine learning and deep learning methods from non-temporal and temporal scenarios, respectively, and comprehensively evaluate the predictive power of the methods for kidney transplantation infection. Specifically, in the non-temporal scenario, we use Naïve Bayes, K-Nearest Neighbor, Support Vector Machines, and Random Forest models. In addition, in the temporal scenario, we propose an MTAN model based on a sliding window algorithm to exploit the hidden information of adjacent time series fully. Experimental results show that the kidney transplantation prediction models built by Naïve Bayes and Support Vector Machines have better stability than those constructed by K-Nearest Neighbor and Random Forest. The MTAN model with sliding windows can better mine the hidden temporal information.
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Gupta, Tarun, and Supriya Bansal. "Machine Learning Algorithms for Predictive Analytics in E-Commerce." International Journal of Science and Research (IJSR) 9, no. 8 (2020): 1550–57. http://dx.doi.org/10.21275/sr24302212009.
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Reena Dhan, Archana, and Binod Kumar. "Machine Learning for Healthcare: Predictive Analytics and Personalized Medicine." International Journal of Science and Research (IJSR) 13, no. 6 (2024): 1307–13. http://dx.doi.org/10.21275/mr24608013906.
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Nordin, Noratikah, Zurinahni Zainol, Mohd Halim Mohd Noor, and Chan Lai Fong. "A comparative study of machine learning techniques for suicide attempts predictive model." Health Informatics Journal 27, no. 1 (2021): 146045822198939. http://dx.doi.org/10.1177/1460458221989395.
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Current suicide risk assessments for predicting suicide attempts are time consuming, of low predictive value and have inadequate reliability. This paper aims to develop a predictive model for suicide attempts among patients with depression using machine learning algorithms as well as presents a comparative study on single predictive models with ensemble predictive models for differentiating depressed patients with suicide attempts from non-suicide attempters. We applied and trained eight different machine learning algorithms using a dataset that consists of 75 patients diagnosed with a depressive disorder. A recursive feature elimination was used to reduce the features via three-fold cross validation. An ensemble predictive models outperformed the single predictive models. Voting and bagging revealed the highest accuracy of 92% compared to other machine learning algorithms. Our findings indicate that history of suicide attempt, religion, race, suicide ideation and severity of clinical depression are useful factors for prediction of suicide attempts.
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Yeole, Vaishali, Rushikesh Yeole, and Pradheep Manisekaran. "Analysis and prediction of stomach cancer using machine learning." Scientific Temper 16, Spl-1 (2025): 131–35. https://doi.org/10.58414/scientifictemper.2025.16.spl-1.16.
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Cancer prediction and analysis systems offer aid in the management of patients and have been found to provide accurate forecasts for stage and survival prediction. This study presents a cancer prediction system developed using machine learning models and implemented with Streamlit. This system is capable of accurately predicting cancer stage onset along with chances of the patient’s onset of survival based on prior patient information. For predictive purposes, categories such as random forest and XGBoost were employed. The model achieved an effective accuracy of 85% for stage prediction and 97% for predictability of patients’ survival. This application includes a simple interface that healthcare professionals can employ to enter patient data and immediately make educated predictions. This paper illustrates the assistance these integrated systems provide clinicians and how they can ameliorate functional healthcare practices. In the future we are hopeful and aim towards further increasing the strength and efficiency of the system by enhancing the dataset used and additional predictive models.
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Dimitrovska, Ivana, and Toni Malinovski. "Creating a Business Value while Transforming Data Assets using Machine Learning." Computer Engineering and Applications Journal 6, no. 2 (2017): 59–70. http://dx.doi.org/10.18495/comengapp.v6i2.205.
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Machine learning enables computers to learn from large amounts of data without specific programming. Besides its commercial application, companies are starting to recognize machine learning importance and possibilities in order to transform their data assets into business value. This study explores integration of machine learning into business core processes, while enabling predictive analytics that can increase business values and provide competitive advantage. It proposes machine learning algorithm based on regression analysis for a business solution in large enterprise company in Macedonia, while predicting real-value outcome from a given array of business inputs. The results show that most of the machine learning predictive values for the desired process output deviated from 0 to 15% of actual employees' decision. Hence, it verifies the appropriateness of the chosen approach, with predictive accuracy that can be meaningful in practice. As a machine learning case study in business context, it contains valuable information that can help companies understand the significance of machine learning for enterprise computing. It also points out some potential pitfalls of machine learning misuse.
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P. Karthik, P. Jayanth, K. Tharun Nayak, and K. Anil Kumar. "Crime Prediction Using Machine Learning and Deep Learning." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 3 (2024): 08–15. http://dx.doi.org/10.32628/ijsrset241134.
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The utilization of machine learning and deep learning methods for crime prediction has become a focal point for researchers, aiming to decipher the complex patterns and occurrences of crime. This review scrutinizes an extensive collection of over 150 scholarly articles to delve into the assortment of machine learning and deep learning techniques employed in forecasting criminal behaviour. It grants access to the datasets leveraged by researchers for crime forecasting and delves into the key methodologies utilized in these predictive algorithms. The study sheds light on the various trends and elements associated with criminal behaviour and underscores the existing deficiencies and prospective avenues for advancing crime prediction precision. This thorough examination of the current research on crime forecasting through machine learning and deep learning serves as an essential resource for scholars in the domain. A more profound comprehension of these predictive methods will empower law enforcement to devise more effective prevention and response strategies against crime.
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Khalate, Vaishnavi, and Prof Borate Sukeshkumar. "Diabetes Prediction Using Machine Learning Algorithm." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 1963–69. http://dx.doi.org/10.22214/ijraset.2024.59256.
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bstract: Diabetes, a chronic metabolic disorder affecting millions worldwide, requires early detection and management to mitigate its complications. Machine learning (ML) techniques have emerged as promising tools for predictive analytics in healthcare, offering the potential to improve diagnostic accuracy and patient outcomes. This paper presents a comprehensive review of ML algorithms applied to diabetes prediction, encompassing diverse methodologies and datasets. The study evaluates the performance of various ML algorithms, including but not limited to logistic regression, decision trees, support vector machines, random forests, and deep learning approaches, in predicting the onset or progression of diabetes. Additionally, feature selection techniques and data pre-processing methods are explored to enhance model robustness and interpretability. Furthermore, this review highlights the significance of dataset characteristics such as size, imbalance, and feature diversity in influencing model performance. Challenges associated with model interpretability, scalability, and deployment in clinical settings are also discussed, alongside potential strategies to address these issues. The findings suggest that ML algorithms demonstrate promising capabilities in diabetes prediction, with many studies reporting high accuracy, sensitivity, and specificity. However, there remains a need for standardized evaluation metrics and benchmark datasets to facilitate comparisons across studies. Moreover, efforts to enhance model interpretability and address data privacy concerns are crucial for promoting the adoption of ML-based predictive models in healthcare practice. In conclusion, this review underscores the potential of ML techniques in diabetes prediction and emphasizes the importance of interdisciplinary collaboration between data scientists, clinicians, and healthcare stakeholders to leverage these advancements for improved patient care and disease management
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R. Patil, Chaitali, Sanika K. Jadhav, Asmeeta L. Bardiya, Ankita P. Davande, and Mahee P. Raverkar. "Machine Learning-Based Predictive Maintenance of Industrial Machines." International Journal of Computer Trends and Technology 71, no. 3 (2023): 50–56. http://dx.doi.org/10.14445/22312803/ijctt-v71i3p108.
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Ren, Zhongjun, Hu Zhang, and Tao Huang. "Application of Transfer Learning Models based on Multi-layer LSTM Deep Learning Framework in Cooling Prediction." Journal of Physics: Conference Series 3001, no. 1 (2025): 012020. https://doi.org/10.1088/1742-6596/3001/1/012020.
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Abstract Data-driven machine learning models have found extensive application in predicting building cooling loads, relying on substantial historical operational data to ensure prediction accuracy. Yet, challenges arise when specific buildings lack sufficient historical operational data, leading to subpar cooling loads prediction performance. Transfer learning emerges as a solution to overcome this limitation by leveraging data from alternative buildings. The hourly cooling load data of two hospital buildings were used as source domain and target domain to verify the transfer learning load prediction based on LSTM deep learning framework. Results indicated that with the increase of the number of sample size, the predictive performance of the target domain gradually becomes stable and reliable. Compared with the transfer learning based on traditional machine learning algorithms, the predictive performance of the transfer learning model adopted in this study is improved by more than 20%.
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Noori Mohammad Ali, Sara, and Nawzad Muhammed Ahmed. "Comparing some Machine Learning Models for Cardiovascular Disease." Journal of Pioneering Medical Sciences 14, no. 04 (2025): 60–66. https://doi.org/10.47310/jpms2025140408.
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Background and Aim: Cardiovascular disease remains a leading cause of morbidity and mortality worldwide, necessitating the development of accurate predictive models for early diagnosis. Therefore, this study aimed to evaluate and compare the performance of three machine learning models-Random Forest, Decision Tree, and K-Nearest Neighbors-in predicting cardiovascular disease based on key risk factors. Method: This retrospective study utilized patient data from Shar Hospital in Sulaimaniyah City. The dataset included demographic and clinical risk factors such as age, smoking status, diabetes, hypertension, and family history of cardiovascular disease. The three machine learning models were trained and tested using various data-splitting ratios, and their performance was assessed using accuracy, F1-score, recall, precision, and specificity. Statistical analysis and model validation were conducted using Python in Jupyter Notebook. Results: A total of 300 patient records were included in the study. The Random Forest model demonstrated the highest predictive accuracy compared to Decision Tree and K-Nearest Neighbors, consistently outperforming the other models across different training-testing configurations. Feature importance analysis revealed that age and family history were the most influential predictors of cardiovascular disease, whereas gender and marital status had minimal impact. The confusion matrix further confirmed the reliability of the Random Forest model, showing a high number of correctly classified cases with minimal false positives and false negatives. Conclusions: The findings indicate that Random Forest is the most effective model for cardiovascular disease prediction, with strong classification performance and high accuracy. The study also highlights the importance of age and family history as dominant risk factors. These results support the application of machine learning in clinical settings for early detection and risk assessment, enabling better-informed medical interventions.
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Dixit, Ashish, Avadhesh Kumar Gupta, Neelam Chaplot, and Veena Bharti. "Emoji Support Predictive Mental Health Assessment Using Machine Learning: Unveiling Personalized Intervention Avenues." International Journal of Experimental Research and Review 42 (August 30, 2024): 228–40. http://dx.doi.org/10.52756/ijerr.2024.v42.020.
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Mental health disorders, including anxiety, depres-sion, and stress, profoundly impact individuals’ well-being and necessitate effective early detection for timely intervention. This research investigates the predictive capabilities of machine learning algorithms in assessing anxiety, depression, and stress levels based on questionnaire-derived scores. Utilizing a dataset comprising self-reported scores obtained through a tailored questionnaire designed for mental health assessment, we delve into the application of Decision Trees, Naive Bayes, Support Vector Machines (SVM), and Random Forests for prediction. Data preprocessing involved comprehensive cleaning, encoding categorical variables, and careful feature selection, ensuring the relevance of features in the predictive models. Each algorithm un-derwent individual implementation, wherein we scrutinized their performances in predicting mental health conditions. Evaluation metrics such as accuracy, precision, and recall were employed to assess the models’ proficiency in predicting anxiety, depression, and stress levels. The findings underscore the potential of machine learning in accurately predicting mental health conditions based on questionnaire responses, offering insights into personalized interventions and early detection systems. This study contributes to advancing the understanding of machine learning applications in mental health assessment, highlighting avenues for impactful interventions in mental health care.
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Amina H., Katu. "Integration of Machine Learning in Predictive Health Diagnostics." RESEARCH INVENTION JOURNAL OF SCIENTIFIC AND EXPERIMENTAL SCIENCES 4, no. 3 (2024): 1–7. https://doi.org/10.59298/rijses/2024/4317.
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The integration of machine learning (ML) into predictive health diagnostics is revolutionizing healthcare by enabling early detection, personalized treatment, and resource optimization. By leveraging large datasets, advanced algorithms, and interdisciplinary collaborations, predictive diagnostics empower healthcare providers to identify risks and manage diseases proactively. This paper investigates the fundamentals of predictive health diagnostics and ML, emphasizing their applications in disease prediction, risk stratification, and personalized medicine. It also addresses challenges such as data quality, privacy, and ethical considerations, offering solutions to foster responsible implementation. Emerging trends, such as real-time analytics and wearable-driven monitoring, are discussed, highlighting the potential for a paradigm shift toward preventive healthcare. The findings emphasize the necessity of a collaborative effort among technologists, clinicians, and policymakers to overcome barriers and harness the transformative potential of ML in predictive health diagnostics. Keywords: Predictive health diagnostics, Machine learning, Artificial intelligence, Personalized medicine, Disease prediction.