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Journal articles on the topic 'Predictive Healthcare Modeling'
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Katreddy, Venkata Senareddy. "Predicting Risks in Healthcare Claims Using Advanced Data Processing and Machine Learning Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40802.
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Healthcare providers and insurers face significant challenges in managing claims, particularly in detecting fraudulent activities and predicting high-cost claims. This paper proposes a methodology for predicting risks in healthcare claims using data analysis and machine learning techniques. By processing large-scale claims data, analyzing patterns, and building predictive models, this approach aims to improve risk management, operational efficiency, and cost savings. Keywords: Healthcare Claims, Risk Prediction, Data Analysis, Predictive Modeling
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G. Ramachandra Rao and Mr.P.Venkata Siva, Dr K. R. R. Mohana Rao, Dr K. Kiran Kumar,. "Use of Predictive Modeling in Healthcare." International Journal for Modern Trends in Science and Technology 6, no. 8S (2020): 156–59. http://dx.doi.org/10.46501/ijmtstciet30.
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Chioma, Susan Nwaimo, Enoch Adegbola Ayodeji, and Daniel Adegbola Mayokun. "Transforming healthcare with data analytics: Predictive models for patient outcomes." GSC Biological and Pharmaceutical Sciences 27, no. 3 (2024): 025–35. https://doi.org/10.5281/zenodo.13383612.
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Healthcare organizations are increasingly leveraging data analytics to improve patient outcomes and enhance the efficiency of healthcare delivery. Predictive modeling, in particular, has emerged as a powerful tool for forecasting patient outcomes based on various data sources such as electronic health records, wearable devices, and genetic information. This paper provides an overview of the transformative role of data analytics in healthcare, with a specific focus on predictive models for patient outcomes. The introduction discusses the importance of data analytics in healthcare and outlines the purpose of the paper. It highlights the evolution of data analytics in healthcare, types of healthcare data, and challenges in data collection and management. The role of predictive modeling in healthcare is then explored, emphasizing its significance in improving patient outcomes and common techniques used in predictive modeling. The paper discusses various data sources for predictive modeling, including electronic health records, wearable devices, genetic and genomic data, and social determinants of health. It also covers the process of developing predictive models, including data preprocessing, model selection, and validation techniques, as well as ethical considerations. Furthermore, the paper explores the applications of predictive models in healthcare, such as early disease detection, personalized treatment planning, hospital resource optimization, and patient engagement. Case studies and examples illustrate real-world implementations of predictive analytics in healthcare organizations. Finally, the paper addresses challenges and future directions in healthcare data analytics, including data privacy and security concerns, interpretability of predictive models, integration into clinical workflows, and emerging trends. Overall, this paper underscores the transformative potential of data analytics, particularly predictive modeling, in revolutionizing healthcare delivery and improving patient outcomes.
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Waheed, Shaikh Abdul, and P. Sheik Abdul Khader. "Healthcare Solutions for Children Who Stutter Through the Structural Equation Modeling and Predictive Modeling by Utilizing Historical Data of Stuttering." SAGE Open 11, no. 4 (2021): 215824402110581. http://dx.doi.org/10.1177/21582440211058195.
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Earlier studies established the role of demographic and temperamental features (DTFs) in the adaptation of childhood stuttering. However, these studies have been short on examining the latent interrelationships among DTFs and not utilizing them in predicting this disorder. This research article endeavors to examine latent interrelationships among DTFs in relation to childhood-stuttering. The purpose of the present is also to analyze whether DTFs can be utilized in predicting the likely risk of this speech disorder. Historical data on childhood stuttering was utilized for performing the invloved experiments of this research. “Structural-Equation-Modeling” (SEM) was applied to examine latent interrelationships among DTFs in relation to stuttering. The predictive analytics approach was employed to ensure whether DTFs of children can be utilized for predicting the likely risk of childhood-stuttering. SEM-based path analysis explored potential latent interrelationships among DTFs by separating them into categories of background and intermediate. By utilizing the same set of the DTFs, predictive models were able to classify children into stuttering and non-stuttering groups with optimal prediction accuracy. The outcomes of this study showed how the stuttering related historical data can be utilized in offering healthcare solutions for individuals with stuttering disorder. The outcomes of the present study also suggest that historical data on stuttering is a very rich source of hidden trends and patterns concerning this disorder. These hidden trends and patterns can be captured by applying a different type of structural and predictive modeling to understand the cause-and-effect relationship among variables in relation to stuttering. The SEM utilizes the cause-and-effect relationship among variables to explore latent-interrelationships between them. While predictive modeling utilizes the cause-and-effect relationship among variables to predict the possible risk of stuttering with optimal prediction accuracy.
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Chukwuka Emmanuel Eze, Geneva Tamunobarafiri Igwama, Ejike Innocent Nwankwo, and Ebube Victor Emeihe. "Predictive modeling for healthcare needs in the aging U.S. population: A conceptual exploration." Global Journal of Research in Science and Technology 2, no. 2 (2024): 094–102. http://dx.doi.org/10.58175/gjrst.2024.2.2.0074.
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The aging population in the United States poses significant challenges for healthcare systems, necessitating advanced strategies to anticipate and meet their healthcare needs. This review paper explores the potential of predictive modeling to address these challenges, offering a conceptual framework that integrates diverse data sources, including electronic health records (EHRs) and social determinants of health (SDOH). Key predictive modeling techniques, such as machine learning and statistical methods, are examined for their application in predicting patient outcomes, disease prevalence, and resource allocation. The paper also highlights the challenges of data privacy, model accuracy, and ethical considerations in the deployment of predictive models. Recommendations for future research emphasize the need for advanced modeling techniques, improved integration of SDOH, and the development of ethical and regulatory frameworks. By leveraging predictive modeling, healthcare systems can enhance their capacity to manage the complex health needs of an aging population, ultimately improving patient outcomes and optimizing resource allocation.
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Chioma Susan Nwaimo, Ayodeji Enoch Adegbola, and Mayokun Daniel Adegbola. "Transforming healthcare with data analytics: Predictive models for patient outcomes." GSC Biological and Pharmaceutical Sciences 27, no. 3 (2024): 025–35. http://dx.doi.org/10.30574/gscbps.2024.27.3.0190.
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Healthcare organizations are increasingly leveraging data analytics to improve patient outcomes and enhance the efficiency of healthcare delivery. Predictive modeling, in particular, has emerged as a powerful tool for forecasting patient outcomes based on various data sources such as electronic health records, wearable devices, and genetic information. This paper provides an overview of the transformative role of data analytics in healthcare, with a specific focus on predictive models for patient outcomes. The introduction discusses the importance of data analytics in healthcare and outlines the purpose of the paper. It highlights the evolution of data analytics in healthcare, types of healthcare data, and challenges in data collection and management. The role of predictive modeling in healthcare is then explored, emphasizing its significance in improving patient outcomes and common techniques used in predictive modeling. The paper discusses various data sources for predictive modeling, including electronic health records, wearable devices, genetic and genomic data, and social determinants of health. It also covers the process of developing predictive models, including data preprocessing, model selection, and validation techniques, as well as ethical considerations. Furthermore, the paper explores the applications of predictive models in healthcare, such as early disease detection, personalized treatment planning, hospital resource optimization, and patient engagement. Case studies and examples illustrate real-world implementations of predictive analytics in healthcare organizations. Finally, the paper addresses challenges and future directions in healthcare data analytics, including data privacy and security concerns, interpretability of predictive models, integration into clinical workflows, and emerging trends. Overall, this paper underscores the transformative potential of data analytics, particularly predictive modeling, in revolutionizing healthcare delivery and improving patient outcomes.
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Meek, Julie A. "10 Ways Predictive Modeling Is Changing Healthcare." CIN: Computers, Informatics, Nursing 27, no. 5 (2009): 334. http://dx.doi.org/10.1097/01.ncn.0000360475.69906.24.
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Ahmad, Ayas. "Predictive Analytics in Healthcare." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 5624–26. http://dx.doi.org/10.22214/ijraset.2024.62897.
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Abstract: This paper delves into the core algorithms and techniques employed in healthcare predictive analytics, including machine learning, statistical modeling, and data mining. We explore the multifaceted applications of this technology, encompassing improved patient stratification for risk assessment, targeted interventions for disease prevention, and optimized resource allocation for healthcare systems. However, the implementation of predictive analytics necessitates careful consideration of ethical issues surrounding data privacy and potential biases within algorithms. Regulatory frameworks may also require adaptation to ensure responsible use of this technology, this research emphasizes the transformative potential of predictive analytics in healthcare, paving the way for a future of proactive medicine and personalized care.
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Sharma, Vishal. "Integrating Machine Learning in Healthcare: Predictive Modeling for Mortality, Heart Failure, and Hospital Readmissions." South Asian Research Journal of Applied Medical Sciences 7, no. 01 (2025): 16–23. https://doi.org/10.36346/sarjams.2025.v07i01.003.
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Machine learning has emerged as a transformative tool in healthcare, enabling predictive analytics for disease progression, patient management, and clinical decision-making. This study integrates three critical areas: mortality trends in the USA, heart failure survival prediction using machine learning (ML) models, and hospital readmission forecasting with artificial intelligence (AI)-driven methodologies. Using datasets from national health statistics, clinical trial data, and electronic health records, this research applies Logistic Regression, Random Forest, Support Vector Machines (SVM), Neural Networks, and Gradient Boosting models to enhance prediction accuracy. Results indicate that SVM achieves the highest predictive accuracy for heart failure survival (88.41%), while Gradient Boosting performs best for readmission prediction. Findings highlight ML’s potential in improving risk stratification, resource allocation, and targeted interventions, contributing to a growing body of AI applications in healthcare analytics. This study provides a foundation for future research on personalized medicine and predictive healthcare models, with broader implications for disease prevention and healthcare efficiency.
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Oh, Tae Ryom. "Integrating predictive modeling and causal inference for advancing medical science." Childhood Kidney Diseases 28, no. 3 (2024): 93–98. http://dx.doi.org/10.3339/ckd.24.018.
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Artificial intelligence (AI) is revolutionizing healthcare by providing tools for disease prediction, diagnosis, and patient management. This review focuses on two key AI methodologies in healthcare: predictive modeling and causal inference. Predictive models excel in identifying patterns to forecast outcomes but are limited in explaining the underlying causes. In contrast, causal inference focuses on understanding cause-and-effect relationships, which makes effective medical interventions possible. Although randomized controlled trials (RCTs) are the gold standard for causal inference, they face limitations including cost and ethical concerns. As alternatives, emulated RCTs and advanced machine learning techniques have emerged for estimating causal effects, bridging the gap between prediction and causality. Additionally, Shapley values and Local Interpretable Model-Agnostic Explanations improve the interpretability of complex AI models, making them more actionable in clinical settings. Integrating prediction and causal inference holds great promise for advancing personalized medicine, enhancing patient outcomes, and optimizing healthcare delivery. However, careful application of AI tools is crucial to avoid misinterpretation and maximize their potential.
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Ibrahim Adedeji Adeniran, Christianah Pelumi Efunniyi, Olajide Soji Osundare, and Angela Omozele Abhulimen. "Data-driven decision-making in healthcare: Improving patient outcomes through predictive modeling." International Journal of Scholarly Research in Multidisciplinary Studies 5, no. 1 (2024): 059–67. http://dx.doi.org/10.56781/ijsrms.2024.5.1.0040.
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This review paper explores the transformative role of data-driven decision-making in healthcare, focusing on how predictive modeling enhances patient outcomes. Predictive modeling techniques have evolved significantly over the years. They are now integral to healthcare operations, aiding in early diagnosis, personalized treatment, and chronic disease management. Despite its potential, implementing predictive modeling faces challenges, including data privacy concerns, integration with existing systems, and potential biases. This paper also examines emerging trends, such as the integration of AI, real-time data from wearable devices, and advancements in genomics, that are driving the future of predictive modeling. Furthermore, the review highlights the need for ongoing research in areas like explainable AI, data interoperability, and privacy protection to realize the full benefits of predictive modeling in healthcare. Predictive modeling can play a crucial role in improving patient outcomes and advancing precision medicine by addressing these challenges and leveraging new technological advancements.
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Toma, Milan, and Ong Chi Wei. "Predictive Modeling in Medicine." Encyclopedia 3, no. 2 (2023): 590–601. http://dx.doi.org/10.3390/encyclopedia3020042.
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Predictive modeling is a complex methodology that involves leveraging advanced mathematical and computational techniques to forecast future occurrences or outcomes. This tool has numerous applications in medicine, yet its full potential remains untapped within this field. Therefore, it is imperative to delve deeper into the benefits and drawbacks associated with utilizing predictive modeling in medicine for a more comprehensive understanding of how this approach may be effectively leveraged for improved patient care. When implemented successfully, predictive modeling has yielded impressive results across various medical specialities. From predicting disease progression to identifying high-risk patients who require early intervention, there are countless examples of successful implementations of this approach within healthcare settings worldwide. However, despite these successes, significant challenges remain for practitioners when applying predictive models to real-world scenarios. These issues include concerns about data quality and availability as well as navigating regulatory requirements surrounding the use of sensitive patient information—all factors that can impede progress toward realizing the true potential impact of predictive modeling on improving health outcomes.
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Panda, Nihar Ranjan, Jitendra Kumar Pati, and Ruchi Bhuyan. "Role of Predictive Modeling in Healthcare Research: A Scoping Review." International Journal of Statistics in Medical Research 11 (September 19, 2022): 77–81. http://dx.doi.org/10.6000/1929-6029.2022.11.09.
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The huge preponderance of inferences drawn in empirical medical research follows from model-based relations (e.g. regression). Here, we described the role of predictive modeling as a complement to this approach. Predictive models are usually probabilistic model which gives a good quality fit to our data. In medical research, it’s very common to use regression models for predictive purposes. Here in this article, we described the types of predictive modeling (Linear and Non-linear) used in medical research and how effectively the researchers take decisions based on predictive modeling, and what precautions, we have to take while building a predictive model. Finally, we consider a working example to illustrate the effectiveness of the predictive model in healthcare.
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Adeshina, Yusuff Taofeek. "Interoperable IT Architectures Enabling Business Analytics for Predictive Modeling in Decentralized Healthcare Ecosystems." International Journal of Research Publication and Reviews 6, no. 5 (2025): 128–52. https://doi.org/10.55248/gengpi.6.0525.1778.
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Babu, Mr M. Jeevan. "Mental Health Prediction Using Catboost Algorithm." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 3449–53. http://dx.doi.org/10.22214/ijraset.2024.59219.
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Abstract: This study investigates the application of the CatBoost algorithm in predicting mental health outcomes using Python programming language. Mental health prediction is a critical area of research due to its significant impact on individuals and society. Traditional predictive modeling techniques often encounter challenges in handling complex and highdimensional data inherent in mental health datasets. CatBoost , a state- of-the-art gradient boosting algorithm, has shown promise in effectively addressing these challenges by handling categorical variables seamlessly and exhibiting robust performance in various domains. Leveraging its powerful capabilities, this study aims to develop predictive models for mental health outcomes utilizing a comprehensive dataset encompassing diverse socio- demographic, behavioural , and clinical factors. The predictive performance of the CatBoost algorithm will be evaluated and compared against other commonly used machine learning algorithms, demonstrating its effectiveness in accurately predicting mental health outcomes. This research contributes to the advancement of predictive modeling in mental health research and holds potential implications for personalized interventions and resource allocation in mental healthcare systems
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Sunny, Md Nagib Mahfuz, Mohammad Balayet Hossain Saki, Abdullah Al Nahian, et al. "Optimizing Healthcare Outcomes through Data-Driven Predictive Modeling." Journal of Intelligent Learning Systems and Applications 16, no. 04 (2024): 384–402. http://dx.doi.org/10.4236/jilsa.2024.164019.
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Liu, Laura. "Disease Prediction Models Based on Medical Big Data." Theoretical and Natural Science 63, no. 1 (2024): 139–43. http://dx.doi.org/10.54254/2753-8818/2024.17942.
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The advent of big data technology has heralded a transformative era in healthcare, with significant implications for disease prediction. This review article delves into the integration of medical big data in predictive modeling, highlighting the pivotal role of data preprocessing, feature engineering, and machine learning algorithms. We explore the escalating research interest, as evidenced by an upward trend in academic publications from 2010 to 2023. The paper underscores the advantages of big data analytics in healthcare, leading to more accurate and personalized disease predictions. Furthermore, we discuss the importance of interdisciplinary collaboration between data scientists, clinicians, and bioinformaticians in enhancing predictive modeling.
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Sylvester Tafirenyika. "AI in healthcare: Predictive modeling, explainability and clinical impact." World Journal of Advanced Research and Reviews 19, no. 3 (2023): 1700–1718. https://doi.org/10.30574/wjarr.2023.19.3.1986.
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Artificial Intelligence (AI) is revolutionizing our generation's health care model in the context of enhancing precision, effectiveness, and speed of clinical decision-making. This essay presents an overview of how AI technology has been used in the health care industry with specific reference to three most important pillars: predictive modeling, explainable AI (XAI), and their ultimate clinical impact. Predictive modeling methods, driven by machine learning algorithms and big health data, enable disease diagnosis at an earlier stage, risk stratification, and individualized treatment protocols. In the absence of transparency in the majority of AI models, transparency, trust, and accountability problems emerged, particularly in clinical high-risk applications. To counter these issues, the paper delineates the growing role of explainable AI (XAI) as a means for establishing confidence among clinicians, facilitating regulatory compliance, and maintaining ethical standards. The research integrates the latest breakthroughs, challenges, and real-world applications and explains how XAI frameworks can fill the algorithmic prediction-to-human interpretability gap. Other than this, the article also explains the clinical role of AI solutions in maximizing diagnostic accuracy, reducing healthcare disparities, and maximizing resource utilization in various healthcare facilities. As great as boundless potential exists in AI, according to the report, there is a cluster of issues associated with data quality, bias mitigation, model explainability, and clinical validation that need to be solved to support solid and credible implementation. Ethically based AI over the long term based on clinical transparency, fairness, and effectiveness within the clinical environment will be the foundation of transformative patient outcomes.
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Bollina, Ganesh. "Predictive Analytics in Healthcare: Leveraging Machine Learning through Salesforce’s Einstein Studio." European Journal of Computer Science and Information Technology 13, no. 47 (2025): 158–71. https://doi.org/10.37745/ejcsit.2013/vol13n47158171.
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The article explores how predictive analytics is reshaping healthcare, especially by allowing medical facilities to use advanced AI. It discusses how, through the advancement of proactive healthcare, predictive tools help with disease progression, predicting risk of hospital readmission, response to treatments, and managing healthcare resources. Things to think about technically are structuring the architecture, combining various systems, ways of modeling, deployment, and security for health-related data. Such strategies handle readiness in the organization, oversee data governance, integrate health records, manage change, and calculate ROI. Such environments give the chance to healthcare professionals in community hospitals and outpatient networks beyond academic centers to build predictive models that benefit their patients and work environment.
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Nwoke, Judith. "Healthcare Data Analytics and Predictive Modelling: Enhancing Outcomes in Resource Allocation, Disease Prevalence and High-Risk Populations." International Journal of Health Sciences 7, no. 7 (2024): 1–35. http://dx.doi.org/10.47941/ijhs.2245.
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Purpose: This study aims to explore the role of healthcare data analytics and predictive modeling in enhancing healthcare outcomes, specifically in resource allocation, disease forecasting, and identifying high-risk populations. Methodology: The research employs a comprehensive approach, utilizing various sources of healthcare data such as electronic health records (EHRs) and public health databases. Advanced analytical techniques, including machine learning, artificial intelligence, and big data analytics, are applied to derive actionable insights. Findings: The study reveals that predictive modeling significantly enhances resource optimization, enables accurate disease prevalence forecasting, and improves the identification of high-risk populations. Case studies demonstrate how these technologies lead to more efficient healthcare delivery, cost reduction, and better patient care outcomes. Unique Contribution to Theory, Policy, and Practice: This research contributes to the theoretical understanding of healthcare data analytics by integrating advanced predictive modeling techniques with real-world healthcare applications. It offers valuable insights for policymakers on the importance of investing in data infrastructure and promoting data-driven decision-making. Practically, the study provides healthcare organizations with actionable strategies to implement predictive analytics for improved resource allocation and patient care.
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Panda, Nihar Ranjan. "A Review on Logistic Regression in Medical Research." National Journal of Community Medicine 13, no. 4 (2022): 265–70. http://dx.doi.org/10.55489/njcm.134202222.
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In today’s scenarios many healthcare decisions are being taken by predictive modeling and machine learning techniques. With this review, we focused on logistic regression model, a kind of predictive modeling used in machine learning, and how healthcare researchers take decisions by the help of predictive modeling. For a better data analysis in healthcare, we need to understand the concept of logistic regression as well as others terms, which are linked with it. so that we can clearly understand the concept behind it and implement in medical research. In this review we worked on an example and illustrated how to perform logistic regression using R programming language. The aim of this paper is to understand logistic regression in healthcare and implement it for decision making.
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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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Tashin, Azad, and Alamgir Islam Md. "Health Data Analytics and Predictive Modeling: Exploring how advanced data analytics and predictive modeling can enhance decision-making in healthcare management, improve patient outcomes, and optimize resource allocation." International Journal of Novel Research in Healthcare and Nursing 11, no. 2 (2024): 96–108. https://doi.org/10.5281/zenodo.11485543.
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<strong>Abstract:</strong> The integration of advanced data analytics and predictive modeling in healthcare is revolutionizing decision-making processes, improving patient outcomes, and optimizing resource allocation. This paper explores the application of these technologies in healthcare management, focusing on their ability to analyze vast datasets, uncover patterns, and predict future outcomes. We evaluated various predictive models, including Logistic Regression, Decision Trees, Random Forests, Gradient Boosting Machines (GBM), and Neural Networks, using a comprehensive dataset from electronic health records (EHRs). Our findings indicate that Neural Networks and GBM outperformed other models in terms of accuracy, precision, recall, F1-score, and AUC-ROC, demonstrating their robustness in handling complex healthcare data. The study also highlights the importance of data quality, model interpretability, and ethical considerations in the deployment of predictive models. By leveraging these advanced analytical tools, healthcare providers can enhance clinical decision-making, personalize treatment plans, and allocate resources more efficiently. This paper contributes to the ongoing efforts to harness the power of predictive analytics in healthcare, emphasizing the need for further research to overcome existing challenges and fully realize the potential of these technologies. <strong>Keywords:</strong> Advanced Data Analytics, Predictive Modeling, Healthcare Management, Resource Allocation. <strong>Title:</strong> Health Data Analytics and Predictive Modeling: Exploring how advanced data analytics and predictive modeling can enhance decision-making in healthcare management, improve patient outcomes, and optimize resource allocation <strong>Author:</strong> Tashin Azad, Md Alamgir Islam <strong>International Journal of Novel Research in Healthcare and Nursing</strong> <strong>ISSN 2394-7330</strong> <strong>Vol. 11, Issue 2, May 2024 - August 2024</strong> <strong>Page No: 96-108</strong> <strong>Novelty Journals</strong> <strong>Website: www.noveltyjournals.com</strong> <strong>Published Date: 05-June-2024</strong> <strong>DOI: https://doi.org/10.5281/zenodo.11485543</strong> <strong>Paper Download Link (Source)</strong> <strong>https://www.noveltyjournals.com/upload/paper/Health%20Data%20Analytics%20and%20Predictive-05062024-2.pdf</strong>
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Ngartera, Lebede, Mahamat Ali Issaka, and Saralees Nadarajah. "Application of Bayesian Neural Networks in Healthcare: Three Case Studies." Machine Learning and Knowledge Extraction 6, no. 4 (2024): 2639–58. http://dx.doi.org/10.3390/make6040127.
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This study aims to explore the efficacy of Bayesian Neural Networks (BNNs) in enhancing predictive modeling for healthcare applications. Advancements in artificial intelligence have significantly improved predictive modeling capabilities, with BNNs offering a probabilistic framework that addresses the inherent uncertainty and variability in healthcare data. This study demonstrates the real-world applicability of BNNs through three key case studies: personalized diabetes treatment, early Alzheimer’s disease detection, and predictive modeling for HbA1c levels. By leveraging the Bayesian approach, these models provide not only enhanced predictive accuracy but also uncertainty quantification, a critical factor in clinical decision making. While the findings are promising, future research should focus on optimizing scalability and integration for real-world applications. This work lays a foundation for future studies, including the development of rating scales based on BNN predictions to improve clinical outcomes.
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Okonkwo, Chidimma. "Predictive Modeling of Healthcare Costs Using Demographic and Health Data in Nigeria." Journal of Statistics and Actuarial Research 8, no. 1 (2024): 1–11. http://dx.doi.org/10.47604/jsar.2753.
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Purpose: The aim of the study was to analyze the predictive modeling of healthcare costs using demographic and health data in Nigeria. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Predictive modeling of healthcare costs using demographic and health data in Nigeria reveals key predictors such as age, socioeconomic status, and comorbidity burden. These models demonstrate high accuracy in forecasting healthcare expenditures, suggesting potential improvements in resource management and patient care. Integrating predictive analytics into healthcare policy could optimize financial planning and enhance overall healthcare delivery despite existing data challenges and infrastructure limitations. Unique Contribution to Theory, Practice and Policy: Health belief model (HBM), agency theory & complex adaptive systems (CAS) theory may be used to anchor future studies on analyze the predictive modeling of healthcare costs using demographic and health data in Nigeria. Develop tools for risk stratification using predictive models, which can assist healthcare providers and insurers in identifying high-risk individuals who may benefit from targeted interventions. Provide evidence-based insights to inform healthcare policy decisions related to resource allocation, reimbursement models, and healthcare financing.
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Wu, Shih-Wei, Cheng-Cheng Li, Te-Nien Chien, and Chuan-Mei Chu. "Integrating Structured and Unstructured Data with BERTopic and Machine Learning: A Comprehensive Predictive Model for Mortality in ICU Heart Failure Patients." Applied Sciences 14, no. 17 (2024): 7546. http://dx.doi.org/10.3390/app14177546.
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Heart failure remains a leading cause of mortality worldwide, particularly within Intensive Care Unit (ICU)-patient populations. This study introduces an innovative approach to predicting ICU mortality by seamlessly integrating electronic health record (EHR) data with a BERTopic-based hybrid machine-learning methodology. The MIMIC-III database serves as the primary data source, encompassing structured and unstructured data from 6606 ICU-admitted heart-failure patients. Unstructured data are processed using BERTopic, complemented by machine-learning algorithms for prediction and performance evaluation. The results indicate that the inclusion of unstructured data significantly enhances the model’s predictive accuracy regarding patient mortality. The amalgamation of structured and unstructured data effectively identifies key variables, enhancing the precision of the predictive model. The developed model demonstrates potential in improving healthcare decision-making, elevating patient outcomes, and optimizing resource allocation within the ICU setting. The handling and application of unstructured data emphasize the utilization of clinical narrative records by healthcare professionals, elevating this research beyond the traditional structured data predictive tools. This study contributes to the ongoing discourse in critical care and predictive modeling, offering valuable insights into the potential of integrating unstructured data into healthcare analytics.
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Murris, Juliette. "Tree-Based Approaches for Interpretable Modeling in Healthcare." Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 7, no. 2 (2025): 37–39. https://doi.org/10.1609/aies.v7i2.31904.
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Survival analysis models time-to-event data in oncology, such as cancer relapse or death, to evaluate treatment effects. Machine learning (ML) advancements, particularly ensemble methods like random survival forests (RSF), enhance predictive accuracy but often lack interpretability, posing challenges for clinical trust and regulatory compliance. This work addresses these limitations by systematically reviewing health authority criteria for AI interpretability, assessing existing methods like SurvSHAP and SurvLIME, and developing an RSF extension to handle multiple clinical events with novel metrics for performance evaluation. Future efforts focus on integrating model-specific interpretability through TreeSHAP and SurvSHAP to provide robust, time-dependent explanations, enabling the alignment of predictive power with clinical transparency in oncology care.
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B, Mohamed Nowfal. "Smart Health Prediction Using Data Mining." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 1219–25. https://doi.org/10.22214/ijraset.2025.68454.
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This project focuses on developing a smart health prediction system using data mining techniques to enhance early detection and prevention of heart disease. By integrating electronic health records, medical databases, and wearable device data, the system leverages classification, clustering, and predictive modeling to identify key risk factors and estimate disease likelihood. The proposed approach enables healthcare providers to make informed decisions, personalize treatment plans, and implement proactive interventions, ultimately improving patient outcomes and reducing healthcare costs. This research contributes to the advancement of data-driven healthcare solutions, fostering precision medicine and predictive analytics in the medical field.
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Nidhi Shashikumar. "Optimizing supply chain efficiency in healthcare using predictive modeling and data analytics." International Journal of Science and Research Archive 15, no. 1 (2025): 1331–41. https://doi.org/10.30574/ijsra.2025.15.1.1107.
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The increasing complexity of healthcare delivery systems, combined with rising patient expectations and global supply chain vulnerabilities, has amplified the urgency to optimize healthcare supply chain management (SCM). Predictive analytics, with its ability to anticipate demand, manage uncertainties, and inform strategic decisions, presents a transformative opportunity for healthcare logistics. This paper explores the foundational concepts of predictive modeling in healthcare SCM, reviews current applications and case studies from global contexts, and identifies key limitations such as data fragmentation, lack of real-time interoperability, and ethical concerns. To address these gaps, a novel Predictive Analytics-Driven Healthcare Supply Chain Optimization (PAD-HSCO) model is proposed, integrating machine learning, real-time data processing, and decision support systems into a cohesive framework. The model is designed to enhance forecasting accuracy, procurement efficiency, and system resilience, particularly in crisis-prone and resource-constrained environments. The study concludes with a discussion on implementation challenges, ethical considerations, and future research directions, underscoring the need for interdisciplinary collaboration to harness predictive analytics in building more sustainable, adaptive, and patient-centric healthcare supply chains.
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Khedekar, Lokesh. "Predictive Modeling for Healthcare Insurance Costs Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 3602–8. https://doi.org/10.22214/ijraset.2025.70775.
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Abstract: Insurance policies help to reduce financial losses by covering various risks, including medical expenses. The task of grading academic assignments is typically cumbersome, unequal, and also charged with some human bias due to personal judgment especially when it is subjective, for instance, essays and short hand answers. The paper presents an AI grading system that automates both objective and subjective assignment grading based on state-of-the-art technology. The system includes Optical Character Recognition (OCR) for processing handwriting, NLP models for evaluating essays and textual responses, and machine learning algorithms for objective questions in multiple-choice and fill-in-the-blank formats. This system also delivers detailed feedback to improve learning outcomes. After significantly reducing grading time while remaining fair and accurate, this system presents a scalable and efficient solution for modernization in educational evaluation processes
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Vaidya, Ayush. "Predictive Modeling for Heart Diseases Detection." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 560–63. https://doi.org/10.22214/ijraset.2025.70225.
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In this Research paper focuses on the development and application of predictive modeling techniques for the early detection of heart disease. Heart disease remains a leading cause of death globally, making early diagnosis and prevention essential. This project seeks to develop a reliable system for predicting the risk of heart disease by utilizing modern machine learning and data analysis techniques, drawing on patient data such as demographics, lifestyle habits, medical background, and clinical test results. By applying various predictive algorithms, such as decision trees, support vector machines, and deep learning models, the system is trained to identify patterns and correlations within the dataset that are indicative of potential cardiovascular issues. The project also emphasizes the use of feature selection techniques to enhance model accuracy and efficiency while mitigating overfitting. The end goal is to create an automated, real-time decision support tool for healthcare providers, enabling them to diagnose heart disease risk more effectively and provide timely interventions
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Ehizogie Paul Adeghe, Chioma Anthonia Okolo, and Olumuyiwa Tolulope Ojeyinka. "A review of the use of machine learning in predictive analytics for patient health outcomes in pharmacy practice." Open Access Research Journal of Life Sciences 7, no. 1 (2024): 052–58. http://dx.doi.org/10.53022/oarjls.2024.7.1.0026.
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Predictive analytics, empowered by machine learning, has emerged as a transformative force in healthcare, offering unparalleled opportunities for enhancing patient outcomes. The primary focus is on understanding the implications, applications, and challenges associated with the use of machine learning algorithms in predicting patient health outcomes. The paper begins by establishing the context with an overview of predictive analytics in healthcare and its evolution. Emphasis is placed on the critical role of patient health outcomes in pharmacy practice. The review explores the current landscape of predictive analytics in pharmacy practice, detailing traditional approaches, their limitations, and the advantages that machine learning brings to the forefront. An in-depth examination of applications follows, focusing on areas such as medication adherence prediction, disease progression modeling, and personalized medication regimens. Real-world case studies and success stories illustrate the practical impact of machine learning on patient outcomes. Addressing the importance of data sources, the paper discusses the diverse types of data employed in predictive analytics, ranging from electronic health records to patient-generated data and wearables. Ethical and privacy concerns are thoroughly explored, emphasizing the need for responsible data usage. The implications for pharmacists and healthcare providers are discussed, highlighting the evolving role of pharmacists in predictive analytics and the potential benefits and challenges for healthcare providers. The conclusion summarizes key findings and issues a call to action, encouraging further research and adoption of machine learning in pharmacy practice to harness its potential for improving patient outcomes.
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Kuwornu, J. P., L. Lix, J. Quail, E. Wang, M. Osman, and G. Teare. "Assessing The Incremental Predictive Value Of Healthcare Utilization Pathways In Risk Prediction Modeling." Value in Health 18, no. 3 (2015): A16. http://dx.doi.org/10.1016/j.jval.2015.03.101.
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Hammad, Uallah, Ali Rija, Ali Saad, and Arif Umair. "Critical Determinants of COVID-19 Severity and Predictive Modeling for Healthcare Optimization." Global Journal of Medical and Clinical Case Reports 12, no. 1 (2025): 004–10. https://doi.org/10.17352/2455-5282.000191.
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The COVID-19 pandemic placed unprecedented strain on global healthcare systems, highlighting the need to identify critical determinants of disease severity and develop predictive models for resource optimization. This study aimed to identify the most significant factors influencing COVID-19 severity, analyze comorbidity patterns, and develop machine learning models for predicting severe outcomes. Using a dataset of 1,000 COVID-19 patients, demographic, clinical, and medical history data were analyzed. Comorbidities such as COPD (96.3%), chronic renal disease (92.6%), cardiovascular issues (93.9%), and diabetes (69.9%) were found to be highly prevalent among severe cases. Over half of the patients required ICU admission (51.1%) or ventilator support (54.5%), indicating the critical impact of severe COVID-19 symptoms on healthcare systems. Four machine learning models decision tree, logistic regression, random forest, and AdaBoost were evaluated for predictive accuracy using a 20-80 ratio and 10-fold cross-validation. In the 20-80 ratio, AdaBoost and logistic regression emerged as the most effective models, achieving 77.00% accuracy, with AdaBoost excelling in precision at 79.84% and specificity at 91.75%, and Logistic Regression providing the highest sensitivity at 67.96% for balanced predictions. The average results across all folds were as follows: Decision Tree accuracy was 65.80%, Random Forest accuracy was 72.40%, Logistic Regression accuracy was 75.40%, and AdaBoost accuracy was 75.50%. These findings underscore the importance of comorbidities in determining COVID-19 severity and demonstrate the utility of predictive modeling in optimizing healthcare resources. The study concludes that tailored interventions for high-risk patients and machine learning-driven resource allocation strategies can enhance healthcare efficiency during pandemics.
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Feng, Mingyang, Xiaosong Wang, Zhiming Zhao, Chufeng Jiang, Jize Xiong, and Ning Zhang. "Enhanced Heart Attack Prediction Using eXtreme Gradient Boosting." Journal of Theory and Practice of Engineering Science 4, no. 04 (2024): 9–16. http://dx.doi.org/10.53469/jtpes.2024.04(04).02.
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Heart attack prediction is a vital component of cardiovascular healthcare, aiming to identify individuals at risk for timely intervention and improved patient outcomes. Despite significant advancements in predictive modeling techniques, several challenges persist, including algorithmic limitations, interpretability issues, data dependence, and scalability concerns. These challenges underscore the need for robust, interpretable, and generalizable predictive models capable of handling the complexities of medical data effectively. In this study, we propose a novel approach leveraging the eXtreme Gradient Boosting (XGBoost) algorithm for heart attack analysis and prediction. We conducted a comprehensive analysis of heart disease datasets, employing rigorous data preprocessing, feature selection, and hyperparameter optimization techniques to develop a highly accurate and interpretable predictive model. Our results demonstrate the efficacy of the XGBoost algorithm in capturing intricate patterns from medical data, achieving superior predictive performance across various metrics. The proposed model addresses the existing challenges in heart attack prediction, offering a promising solution for enhancing cardiovascular healthcare outcomes.
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Siddhartha, Nuthakki. "Exploring the Role of Data Science in Healthcare: From Data Collection to Predictive Modeling." European Journal of Advances in Engineering and Technology 7, no. 11 (2020): 75–79. https://doi.org/10.5281/zenodo.13470691.
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The integration of data science in healthcare has revolutionized the industry, offering innovative solutions for data collection, management, and predictive analytics. This paper explores the multifaceted role of data science in healthcare, from the initial stages of data collection to the implementation of predictive modeling techniques. By examining current methodologies, challenges, and future directions, we aim to highlight the transformative impact of data science on healthcare outcomes.
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Siddhartha, Nuthakki. "Exploring the Role of Data Science in Healthcare: From Data Collection to Predictive Modeling." European Journal of Advances in Engineering and Technology 7, no. 11 (2020): 75–79. https://doi.org/10.5281/zenodo.13470691.
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The integration of data science in healthcare has revolutionized the industry, offering innovative solutions for data collection, management, and predictive analytics. This paper explores the multifaceted role of data science in healthcare, from the initial stages of data collection to the implementation of predictive modeling techniques. By examining current methodologies, challenges, and future directions, we aim to highlight the transformative impact of data science on healthcare outcomes.
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Siddhartha, Nuthakki. "Exploring the Role of Data Science in Healthcare: From Data Collection to Predictive Modeling." European Journal of Advances in Engineering and Technology 7, no. 11 (2020): 75–79. https://doi.org/10.5281/zenodo.13470691.
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The integration of data science in healthcare has revolutionized the industry, offering innovative solutions for data collection, management, and predictive analytics. This paper explores the multifaceted role of data science in healthcare, from the initial stages of data collection to the implementation of predictive modeling techniques. By examining current methodologies, challenges, and future directions, we aim to highlight the transformative impact of data science on healthcare outcomes.
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Siddhartha, Nuthakki. "Exploring the Role of Data Science in Healthcare: From Data Collection to Predictive Modeling." European Journal of Advances in Engineering and Technology 7, no. 11 (2020): 75–79. https://doi.org/10.5281/zenodo.13470691.
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The integration of data science in healthcare has revolutionized the industry, offering innovative solutions for data collection, management, and predictive analytics. This paper explores the multifaceted role of data science in healthcare, from the initial stages of data collection to the implementation of predictive modeling techniques. By examining current methodologies, challenges, and future directions, we aim to highlight the transformative impact of data science on healthcare outcomes.
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Karunakaran, N., Mohammed Sanusi Sadiq, I. P. Singh, M. M. Ahmad, and B. Maryam. "Block chain technology for e-health." Journal of Community Health Management 11, no. 2 (2024): 71–87. http://dx.doi.org/10.18231/j.jchm.2024.014.
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There is a dearth of interoperability between apps, data streams, and predictability in the healthcare industry for a significant amount of the data generated by multiple digital ecosystems. Real-time data streams can be derived as meaningful and scalable enough to enable real-time healthcare predictive analytics thanks to the new technology approach in distributed messaging and Blockchain, which has become a fundamental component of many healthcare technology stacks. Additionally, absorbing data streams from multiple sources from patterns of data can enhance models that are hampered by complex and lengthy analyses by raising the level of prediction and accuracy. Improved responses, lowered availability requirements, and unified predictive modeling will speed up healthcare interoperability and, in turn, improve diagnosis accuracy, move evidence-based medicine (EBM) in the right direction, and produce other positive effects on healthcare that improve best results and quality.
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Mudzramer A. Hayudini, Datu Ansaruddin K. Kiram, Mharcelyn M. Kiram, Abdulkamal H. Abduljalil, Nureeza J. Latorre, and Fahra B. Sahibad. "Predictive Modeling in Cardiovascular Disease: An Investigation of Random Forests." Natural Sciences Engineering and Technology Journal 5, no. 1 (2024): 393–404. https://doi.org/10.37275/nasetjournal.v5i1.60.
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Cardiovascular diseases (CVDs) are a leading cause of death worldwide. Early detection and intervention are crucial for improving patient outcomes. Machine learning (ML) offers promising tools for CVD prediction, with random forests (RF) emerging as a robust and versatile algorithm. This study investigates the application of RF in predicting blood pressure categories, a crucial indicator of cardiovascular health, using a comprehensive dataset of patient metrics. This study investigated the application of RF in predicting blood pressure categories, a crucial indicator of cardiovascular health. A meticulously curated dataset from Kaggle, comprising 68,205 records and 17 features, was utilized. Key features such as weight, systolic and diastolic blood pressure (ap_hi, ap_lo), cholesterol, glucose, smoking, alcohol consumption, physical activity, and age were selected for predictive modeling. The RF model was trained and tested using a stratified split, and its performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix. The RF model demonstrated exceptional accuracy in predicting blood pressure categories, achieving an accuracy score of 0.9999. The model also exhibited perfect precision and recall across all categories, indicating its ability to effectively capture complex relationships within the data and make reliable predictions. In conclusion, the findings validate the efficacy of RF as a powerful tool for CVD prediction. Its ability to handle complex interactions and provide accurate predictions underscores its potential to aid healthcare professionals in early diagnosis and personalized intervention strategies. Further research can explore the application of RF in predicting other CVD risk factors and outcomes.
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Kadirovna, Muratova Saodat, Shukurova Nodira Tillayevna, Baratov Bobur, and Teshayev Shoxjahon. "PREDICTIVE MODELING OF THE PROBABILITY OF DEVELOPING PERIODONTAL DISEASES IN PATIENTS WITH CARDIOVASCULAR DISEASE." European International Journal of Multidisciplinary Research and Management Studies 4, no. 4 (2024): 65–70. http://dx.doi.org/10.55640/eijmrms-04-04-10.
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Currently, the forecast of the development of pathology is an important part of all branches of healthcare. [3,4,5]. However, despite the importance and scientific and practical significance of forecasting in dentistry, at present we have not found information about predictive models of individual risk of developing periodontitis in patients with hypertension.
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Powers, Christopher A., Christina M. Meyer, M. Christopher Roebuck, and Baze Vaziri. "Predictive Modeling of Total Healthcare Costs Using Pharmacy Claims Data." Medical Care 43, no. 11 (2005): 1065–72. http://dx.doi.org/10.1097/01.mlr.0000182408.54390.00.
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N., Pegu*1 S. Seth2 S. Ramakrishnan3 A. Jangili4. "Healthcare Predictive Modeling for Identifying Fraud in Medical Insurance Claims." International Journal of Pharmaceutical Sciences 3, no. 2 (2025): 1734–44. https://doi.org/10.5281/zenodo.14899939.
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Fraud detection in healthcare insurance claims is of prime importance to financial stability, operational efficiency, and policyholder trust. Rule-based and hand-crafted manual audit checks, which are traditional fraud detection methods, produce low quality false positives and low response rates to emerging trends in fraud schemes. This work proposes an integrated scheme of XAI-based and machine learning-based fraud detection towards improved accuracy, explainability, and real-time fraud detection capability. The article proposes a comparison of machine learning algorithm-based schemes, i.e., Logistic Regression, SVM, Random Forest, KNN, and Autoencoder, on fraudulent healthcare claim detection in artificial National Health Insurance System (NHIS) datasets. Experimentation results indicate that highest precision and accuracy of (1.000 and 88.7%, respectively) are produced by Logistic Regression and SVM, which are highly reliable in minimizing false positives. Based on results presented, it is concluded that an integrated fraud detection scheme, consisting of a supervised and an unsupervised learning scheme, can improve fraud detection accuracy significantly. Except for the healthcare sector, the proposed mechanism can be effectively applied to banks, retailing, e-commerce, telephony, and supply chains, wherever fraud detection capability is of particular concern. In addition to the machine learning algorithm, the article presents prime concerns on data privacy-related issues, model interpretability issues, and associated computational complexities in providing inputs towards future directionality in AI-sustained fraud avoidance.
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Chandran, Kavya. "Optimizing Healthcare Finance: A Predictive Modeling Approach to Medical Insurance Premium." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (2024): 3066–69. http://dx.doi.org/10.22214/ijraset.2024.59574.
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Abstract: This research delves into optimizing healthcare finance through predictive modeling to forecast medical insurance premiums accurately. By harnessing a robust dataset and integrating advanced analytics, this study meticulously constructs models that allow insurance companies to price their policies competitively, ensuring both profitability and fairness. Employing a variety of machine learning algorithms, including linear regression, decision trees, random forests, and gradient boosting, we thoroughly assess the influence of critical factors such as age, BMI, gender, and regional healthcare costs on premium costs. Our analysis not only showcases the precision of predictive modeling in refining insurance pricing strategies and risk management but also illuminates its broader implications for the healthcare insurance sector. By systematically exploring the factors affecting premiums, identifying the most efficacious modeling techniques, and delineating the potential benefits for insurers and policyholders, this paper significantly contributes to the ongoing discourse on leveraging data-driven approaches to enhance the insurance industry's operational efficiency and promote equitable access to healthcare coverage.
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Khodadadi, Ehsaneh, and S. K. Towfek. "Internet of Things Enabled Disease Outbreak Detection: A Predictive Modeling System." Journal of Intelligent Systems and Internet of Things 10, no. 1 (2023): 84–91. http://dx.doi.org/10.54216/jisiot.100107.
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Advancements in data analytics and the proliferation of the Internet of Things (IoT) have opened new frontiers in disease surveillance and early outbreak detection. In this paper, we present a comprehensive framework that integrates IoT-driven predictive data analytics with a secure blockchain network to revolutionize the early warning of disease outbreaks. Our system model comprises edge devices equipped with sensors for data collection and processing, coupled with a blockchain network ensuring data integrity and transparency. Within this framework, we focus on the pivotal role of a Support Vector Machine (SVM) for disease outbreak prediction, showcasing its exceptional accuracy and performance. Through extensive experimentation and comparative analysis, we demonstrate that the SVM, when embedded in our IoT ecosystem, excels in predicting disease outbreaks, outperforming other machine learning models. This approach not only enhances the timeliness and precision of outbreak detection but also facilitates informed decision-making and resource allocation. Furthermore, our system model's integration with blockchain technology ensures the secure storage and validation of prediction results, bolstering the trustworthiness of collected data. This research represents a significant leap forward in proactive disease management and public health, offering a blueprint for future endeavors in epidemiology and healthcare. It underscores the transformative potential of IoT-driven predictive analytics in safeguarding global health and well-being.
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kartheek, J. Pavan. "Predicting Fetal Features from DNA Through Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem41160.
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Predicting fetal features from DNA using machine learning represents a pioneering advancement in prenatal diagnostics and personalized medicine. This research leverages genomic data and advanced machine learning techniques to predict fetal traits, such as physical attributes and potential health conditions, with high precision. By integrating feature selection methods and predictive modeling, the study highlights the potential of machine learning in enabling early diagnosis and personalized healthcare planning. Ethical considerations, including data privacy and responsible use of genetic information, are integral to the project's approach. This work not only advances the understanding of genetic determinants of fetal development but also sets the stage for future innovations in genomics and prenatal healthcare. Keywords— Fetal Feature Prediction, Machine Learning, Genomic Data Analysis, Prenatal Diagnostics, Ethical Genomics, Phenotypic Trait Prediction, Personalized Medicine
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Khalid talib Othman. "Modeling Chronic Disease Prediction Using Statistical Machine Learning Methods." Advances in Nonlinear Variational Inequalities 28, no. 5s (2025): 18–30. https://doi.org/10.52783/anvi.v28.3535.
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Chronic sicknesses, which includes diabetes, cardiovascular conditions, and cancer, pose substantial public fitness challenges worldwide because of their high occurrence and associated mortality fees. Accurate prediction of these illnesses is important for early intervention, improving affected person effects, and reducing healthcare expenses. This have a look at investigates the utility of statistical machine mastering techniques for chronic sickness prediction, leveraging patient information to develop strong predictive fashions. The studies explores a variety of algorithms, together with logistic regression, choice timber, aid vector machines, and ensemble methods like random forests and gradient boosting. The dataset contains anonymized health information, encompassing demographic records, clinical parameters, and lifestyle elements. Feature choice techniques are employed to identify the maximum enormous predictors, enhancing model interpretability and lowering computational complexity. The performance of each model is evaluated using metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve Cross-validation checks the generalizability and robustness of the models. The results show that ensemble methods, especially gradient versting, outperform traditional statistical methods in handling complex nonlinear relationships in the data This study highlights the potential of machine learning in predictive healthcare management, providing tools for risk classification and personalized medical practice for the integration of Machine learning models into clinical practice enables healthcare professionals to make data-driven decisions to improve chronic disease management. Future work will focus on improving models with comprehensive data, introducing more inputs such as integrating genetic information, and addressing ethical concerns about data confidentiality and bias.
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Thu Thu Aung, Thu Thu Aung, Khine Thinzar, and Su Wai Phyo. "A Comparative Analysis of Machine Learning Models in Predicting Blood Donation Behavior." International Journal of Research and Scientific Innovation XII, no. V (2025): 1647–55. https://doi.org/10.51244/ijrsi.2025.120500157.
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The prediction of blood donation behavior is essential for improving donor recruitment and retention strategies within healthcare systems. This study performed a comparative analysis of three machine learning models such as Logistic Regression, Random Forest and Support Vector Machine (SVM) to predict blood donation behavior based on blood donation history data. The primary goal was to conduct a comparative analysis of three machine learning models. The study employed a comprehensive dataset that included various features related to donation history of potential donors. The models were evaluated using several key performance metrics, including accuracy, precision, recall, F1 score, and ROC-AUC, which provide assessing their predictive capabilities. The findings of the analysis indicated that the Random Forest model significantly outperformed the other two algorithms, achieving an accuracy of 92% and a ROC-AUC score of 0.93. This superior performance was attributed to Random Forest’s ability to capture complex interactions within the dataset, making it particularly effective for this type of predictive modeling. In contrast, SVM and Logistic Regression demonstrated lower accuracy and predictive power, highlighting their limitations in this context. The results of this study highlight the potential of machine learning techniques to improve blood donation strategies. By utilizing advanced predictive modeling, healthcare organizations can refine their outreach efforts, ultimately increasing donation rates and addressing critical public health needs. This research contributes to the expanding field of predictive analytics in healthcare, providing valuable insights that can inform future initiatives aimed at improving blood donation behaviors.
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Madhavi Latha Vadlamudi. "Enhancing Predictive Analytics in Healthcare with Big Data Integration." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 445–60. https://doi.org/10.32996/jcsts.2025.7.3.51.
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The healthcare industry is experiencing a transformative shift through the integration of big data analytics and predictive modeling capabilities. The implementation of advanced analytics platforms has revolutionized patient care delivery, operational efficiency, and clinical decision support systems. Machine learning algorithms have demonstrated remarkable capabilities in predicting hospital readmissions, detecting early warning signs of patient deterioration, and optimizing resource allocation across healthcare facilities. The adoption of cloud-based analytics solutions, combined with sophisticated data lake architectures, has enabled healthcare organizations to process and analyze vast quantities of clinical and operational data in real-time. Integration of social determinants of health with traditional clinical indicators has enhanced predictive accuracy and enabled more comprehensive patient risk assessments. Modern healthcare analytics frameworks incorporate federated learning approaches and edge computing solutions, ensuring data privacy while enabling collaborative model development across institutions. Natural language processing capabilities have transformed clinical documentation analysis, while artificial intelligence systems continue to advance diagnostic accuracy and treatment optimization. These technological advancements have resulted in substantial improvements in patient outcomes, operational efficiency, and cost reduction across the healthcare ecosystem.