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Articles de revues sur le sujet « Predictive healthcare »

Auteur : Grafiati

Publié le 2 juin 2025

Mis à jour le 31 juillet 2025

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1

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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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 cutt

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Hariom, Rajput* Sanoop Kumar Tiwari. "Detection Of Diseases And Predictive Analytic." International Journal in Pharmaceutical Sciences 1, no. 11 (2023): 180–91. https://doi.org/10.5281/zenodo.10084620.

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Predictive analytics plays a vital role in transforming healthcare by improving patient care, reducing costs, and optimizing resource allocation. As technology continues to advance and healthcare systems become more data- driven, the benefits of predictive analytics are likely to expand, contributing to better healthcare outcomes for individuals and populations alike. Predictive analytics is transforming the healthcare landscape by enhancing early disease detection and prevention. By harnessing the power of data and artificial intelligence, healthcare providers can offer more personalized, eff

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Boonprasope, Anuwat, and Korrakot Yaibuathet Tippayawong. "Predicting Healthcare Mutual Fund Performance Using Deep Learning and Linear Regression." International Journal of Financial Studies 12, no. 1 (2024): 23. http://dx.doi.org/10.3390/ijfs12010023.

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Following the COVID-19 pandemic, the healthcare sector has emerged as a resilient and profitable domain amidst market fluctuations. Consequently, investing in healthcare securities, particularly through mutual funds, has gained traction. Existing research on predicting future prices of healthcare securities has been predominantly reliant on historical trading data, limiting predictive accuracy and scope. This study aims to overcome these constraints by integrating a diverse set of twelve external factors spanning economic, industrial, and company-specific domains to enhance predictive models.

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Kailash, Alle. "AI in Healthcare: Predictive Analytics and Diagnostics." Journal of Scientific and Engineering Research 7, no. 9 (2020): 233–37. https://doi.org/10.5281/zenodo.13347491.

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Predictive analytics and decision support systems are changing patient care in artificial intelligence (AI) in healthcare. Through the identification of trends and risk variables, predictive analytics ease early illness prevention and diagnosis, improving patient outcomes and enabling cost-effective healthcare. By using unique patient data to create customized therapies that maximize benefits and reduce side effects, machine learning enables individualized treatment strategies. AI-driven algorithms improve diagnostic precision in medical imaging by delivering quick and correct evaluations. Hea

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Cypher, Rebecca L. "Predictive Analysis in Healthcare." Journal of Perinatal & Neonatal Nursing 35, no. 4 (2021): 298–301. http://dx.doi.org/10.1097/jpn.0000000000000605.

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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 framewo

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Sugarwar, Kalyani S., and Santanu Sikdar. "Artificial Intelligence Applications in Predictive Healthcare Systems." Journal of Advances and Scholarly Researches in Allied Education 22, no. 01 (2025): 322–33. https://doi.org/10.29070/s2zg4656.

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Predictive systems made possible by artificial intelligence (AI) are revolutionising healthcare by allowing for more precise, rapid, and individualised medical procedures. Using data analytics, NLP, and machine learning algorithms, this article delves into the ways AI is being applied to predictive healthcare, specifically in the areas of illness risk prediction, treatment plan optimisation, and patient outcome improvement. Using massive datasets derived from genetic information, electronic health records, and real-time monitoring equipment, predictive algorithms seek out trends and outliers t

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de Korte, Maud H., Gertjan S. Verhoeven, Arianne M. J. Elissen, Silke F. Metzelthin, Dirk Ruwaard, and Misja C. Mikkers. "Using machine learning to assess the predictive potential of standardized nursing data for home healthcare case-mix classification." European Journal of Health Economics 21, no. 8 (2020): 1121–29. http://dx.doi.org/10.1007/s10198-020-01213-9.

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Abstract Background The Netherlands is currently investigating the feasibility of moving from fee-for-service to prospective payments for home healthcare, which would require a suitable case-mix system. In 2017, health insurers mandated a preliminary case-mix system as a first step towards generating information on client differences in relation to care use. Home healthcare providers have also increasingly adopted standardized nursing terminology (SNT) as part of their electronic health records (EHRs), providing novel data for predictive modelling. Objective To explore the predictive potential

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Gupta, Saurabh. "The Role of AI in Predictive Healthcare Analytics." International Journal of Science and Research (IJSR) 13, no. 11 (2024): 1760–64. https://doi.org/10.21275/sr241130150603.

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Alajmi, Bibi M., Laila N. Marouf, and Abdus Sattar Chaudhry. "Knowledge Management for Healthcare: Investigating Practices that Drive Performance." Journal of Information & Knowledge Management 15, no. 02 (2016): 1650014. http://dx.doi.org/10.1142/s0219649216500143.

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Knowledge management (KM) is considered an important intervention in improving health care services. KM facilitates the transfer of existing knowledge and the development of new knowledge in hospitals. This research focuses on investigating the relationship between KM practices and performance in selected hospitals in Kuwait, exemplified by perceived productivity and quality. Survey data were collected from 277 doctors working in public and private hospitals in Kuwait. As predicted by previous studies, the doctors who responded to this research perceived good KM practices to have positive corr

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Mohnen, Sigrid M., Adriënne H. Rotteveel, Gerda Doornbos, and Johan J. Polder. "Healthcare Expenditure Prediction with Neighbourhood Variables – A Random Forest Model." Statistics, Politics and Policy 11, no. 2 (2020): 111–38. http://dx.doi.org/10.1515/spp-2019-0010.

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AbstractWe investigated the additional predictive value of an individual’s neighbourhood (quality and location), and of changes therein on his/her healthcare costs. To this end, we combined several Dutch nationwide data sources from 2003 to 2014, and selected inhabitants who moved in 2010. We used random forest models to predict the area under the curve of the regular healthcare costs of individuals in the years 2011–2014. In our analyses, the quality of the neighbourhood before the move appeared to be quite important in predicting healthcare costs (i.e. importance rank 11 out of 126 socio-dem

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Ramirez-Alcocer, Ulises Manuel, Edgar Tello-Leal, Gerardo Romero, and Bárbara A. Macías-Hernández. "A Deep Learning Approach for Predictive Healthcare Process Monitoring." Information 14, no. 9 (2023): 508. http://dx.doi.org/10.3390/info14090508.

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In this paper, we propose a deep learning-based approach to predict the next event in hospital organizational process models following the guidance of predictive process mining. This method provides value for the planning and allocating of resources since each trace linked to a case shows the consecutive execution of events in a healthcare process. The predictive model is based on a long short-term memory (LSTM) neural network that achieves high accuracy in the training and testing stages. In addition, a framework to implement the LSTM neural network is proposed, comprising stages from the pre

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Haritima, Haritima, S. Sakena Benazer, Tatiraju V. Rajani Kanth, and K. Dhineshkumar. "An Adaptive Learning-Driven Software Ecosystem for Optimized Healthcare Solutions with Artificial Intelligence." International Journal of BIM and Engineering Science 09, no. 2 (2024): 45–54. http://dx.doi.org/10.54216/ijbes.090206.

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The use of machine learning methods in healthcare has shown encouraging outcomes in terms of better patient care, more efficient use of resources, and streamlined operations. Traditional machine learning methods encounter difficulties when dealing with healthcare data due to its complexity and heterogeneity. Healthcare applications are a good fit for Gradient Boosting Machines (GBMs), which have become a formidable tool for structured data and predictive modelling jobs. Better healthcare system capabilities, including more precise forecasts and well-informed decisions, may be achieved by the i

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Ratnaprabha Ravindra Borhade. "AI-Enhanced Predictive Analytics for Proactive Healthcare Management: Leveraging Machine Learning to Improve Patient Care and Operational Efficiency." Panamerican Mathematical Journal 35, no. 1s (2024): 46–57. http://dx.doi.org/10.52783/pmj.v35.i1s.2096.

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As healthcare changes quickly, combining artificial intelligence (AI) and machine learning (ML) technologies has become a major force that can change things. This is because it improves predictive analytics for proactive healthcare management. This essay looks at how AI-powered predictive analytics might be able to help healthcare systems provide better care to patients and run more smoothly. Machine learning algorithms can find patterns and trends in huge amounts of patient data that traditional analysis methods might miss. This lets healthcare workers make smart decisions before big problems

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Ogunsanwo, Gbenga O. "PREDICTIVE MODEL FOR HEALTH INSURANCE COST USING SELF-ORGANIZING MAPS AND XGBOOST." FUDMA JOURNAL OF SCIENCES 8, no. 6 (2024): 354–62. https://doi.org/10.33003/fjs-2024-0806-3120.

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Machine Learning (ML) techniques are gaining more adoption in every sector in order to improve their services. The healthcare industry is not left behind in this development of adopting ML predictive model to increase their efficiency and productivity.The paper developed a predictive healthcare insurance cost Model using Self-Organizing Maps (SOM) and XGBoost models. In this study, two models, SOM and XGBoost were deployed for medical insurance cost prediction using the dataset from KAGGLE’s repository which consists of 1338 instances and 7 predicting parameters. The dataset were preprocessed

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Researcher. "LEVERAGING CRITICAL AND EMERGING TECHNOLOGIES FOR PREDICTIVE ANALYTICS IN HEALTHCARE: OPTIMIZING PATIENT OUTCOMES AND RESOURCE ALLOCATION." International Journal of Artificial Intelligence & Machine Learning (IJAIML) 3, no. 2 (2024): 101–10. https://doi.org/10.5281/zenodo.13740104.

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The healthcare industry faces increasing pressure to deliver high-quality patient care while managing limited resources efficiently. Predictive analytics, enabled by critical and emerging technologies (CETs) such as artificial intelligence (AI), machine learning (ML), cloud computing, and the Internet of Things (IoT), is transforming healthcare operations. This paper reviews the application of CETs in predictive analytics to improve patient outcomes and optimize resource allocation. By synthesizing data from academic research, healthcare case studies, and industry reports, we examine the poten

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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 d

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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 eva

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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 landsca

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Reddy Kudumula, Umamaheswara. "Enhancing Healthcare Operations with Predictive Length of Stay Models." International Journal of Science and Research (IJSR) 13, no. 7 (2024): 792–96. http://dx.doi.org/10.21275/sr24715093831.

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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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Inukonda, Jaishankar. "Leveraging Artificial Intelligence for Predictive Insights from Healthcare Data." International Journal of Science and Research (IJSR) 13, no. 10 (2024): 611–15. http://dx.doi.org/10.21275/sr241006040947.

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Y. R, Venugopal, and Dr Srikanth V. "Ensemble Learning Approaches for Improved Predictive Analytics in Healthcare." International Journal of Research Publication and Reviews 5, no. 3 (2024): 757–60. http://dx.doi.org/10.55248/gengpi.5.0324.0629.

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Kingsley Anyaso and Victor Okoye. "The Impact of Big Data and Predictive Analytics on U.S. Healthcare Delivery: Opportunities, Challenges, and Future Directions." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 2275–7787. http://dx.doi.org/10.30574/wjarr.2024.24.1.3266.

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Healthcare, like many other industries, has been significantly influenced by big data and predictive analytics. The vast volume, velocity, and variety of information within big data sets have transformed the way we approach patient care and medical innovation. From predicting disease outbreaks to delivering personalized treatment plans, big data analytics offers immense potential for revolutionizing healthcare. This review explores the significant impact of big data and predictive analytics on the U.S. healthcare system. It delves into hospitals' ability to effectively leverage complex informa

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Kingsley, Anyaso, and Okoye Victor. "The Impact of Big Data and Predictive Analytics on U.S. Healthcare Delivery: Opportunities, Challenges, and Future Directions." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 2275–87. https://doi.org/10.5281/zenodo.15057116.

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Healthcare, like many other industries, has been significantly influenced by big data and predictive analytics. The vast volume, velocity, and variety of information within big data sets have transformed the way we approach patient care and medical innovation. From predicting disease outbreaks to delivering personalized treatment plans, big data analytics offers immense potential for revolutionizing healthcare. This review explores the significant impact of big data and predictive analytics on the U.S. healthcare system. It delves into hospitals' ability to effectively leverage complex informa

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Hemalatha, Sweetlin, and Apurva Waghmare. "PREDICTIVE ANALYTICS OF HEALTHCARE DATA." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (2017): 333. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19750.

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Predictive analytics is employed to improve the ability to take precautionary measures during medical emergencies. In health care, the sensor-baseddata are generated daily which can be used to predict future data using regression model. In this paper, pain dataset from integrating data for analysis,anonimyzation, and sharing repository is used for experimenting different machine algorithms. The results show that logistic regression gives moreaccuracy than other algorithms.

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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), N

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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

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Leelakumar, Raja Lekkala. "Employing Machine Learning For Predictive Data Analytics In Healthcare." International Journal for Science and Advance Research In Technology 9, no. 6 (2023): 157–59. https://doi.org/10.5281/zenodo.10371304.

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One of the most significant innovations in the field technology is machine learning. It has become one of the most powerful tools in data analytics to help scientists gain valuable insights and make informed decisions. The ability to predict future outcomes and take preemptive actions based on that knowledge is what makes machine learning so useful in healthcare. Predictive data analytics can help healthcare organizations identify at-risk patients, improve patient outcomes, optimize resource allocation, and enhance decision-making processes. This research paper explores the application of mach

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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

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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 t

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Singhania, Mr Gaurav, Nikhil Maurya, Parth Sharma, and Ronit Bhardwaj. "Revolutionizing Disease Prediction with Deep Learning and Predictive Analysis." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 2571–77. http://dx.doi.org/10.22214/ijraset.2024.62137.

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Abstract: The integration of deep learning and predictive analysis has emerged as a powerful tool in the healthcare improvement effort, revolutionizing the process of illness prediction. The purpose of this project is to develop a comprehensive framework that can precisely predict and identify a range of diseases at an early stage using these technologies. Deep learning algorithms are used to uncover complicated patterns and connections that may point to the onset of a disease by evaluating massive datasets that include patient demographics, medical history, genetic data, and environmental fac

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T, Ajmal, and Preethi Thomas. "Medicine Recommendation System - A Smart Approach to Predictive Healthcare Solutions." International Journal of Science and Research (IJSR) 14, no. 4 (2025): 1436–41. https://doi.org/10.21275/sr25418004822.

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C, Sushama, Shaik Mohammad Rafee, Jaimala Jha, Sujatha S, Jagadeesan Srinivasan, and Mohana Krishna I. "Enhancing Healthcare Industrial Applications With LSTM-Based Predictive Analytics." Informing Science: The International Journal of an Emerging Transdiscipline 28 (2025): 014. https://doi.org/10.28945/5416.

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Aim/Purpose: This work attempts to investigate the application of Long Short Term Memory (LSTM)-based predictive analytics in the medical field. The scope comprises the development and application of LSTM models to forecast outcomes, including patient diagnosis, treatment responses, healthcare resource consumption, and other relevant variables. Background: Predictive analytics has become popular in many other industries, including healthcare, since it can analyze enormous amounts of data and project future patterns. For LSTM, a variant of encoder-decoder LSTM-based recurrent neural network (RN

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Nancy, A. Angel, Dakshanamoorthy Ravindran, P. M. Durai Raj Vincent, Kathiravan Srinivasan, and Daniel Gutierrez Reina. "IoT-Cloud-Based Smart Healthcare Monitoring System for Heart Disease Prediction via Deep Learning." Electronics 11, no. 15 (2022): 2292. http://dx.doi.org/10.3390/electronics11152292.

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The Internet of Things confers seamless connectivity between people and objects, and its confluence with the Cloud improves our lives. Predictive analytics in the medical domain can help turn a reactive healthcare strategy into a proactive one, with advanced artificial intelligence and machine learning approaches permeating the healthcare industry. As the subfield of ML, deep learning possesses the transformative potential for accurately analysing vast data at exceptional speeds, eliciting intelligent insights, and efficiently solving intricate issues. The accurate and timely prediction of dis

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Researcher. "HEALTHCARE DATA ANALYTICS: LEVERAGING PREDICTIVE ANALYTICS FOR IMPROVED PATIENT OUTCOMES." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 548–65. https://doi.org/10.5281/zenodo.14197001.

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Predictive analytics has emerged as a transformative force in modern healthcare, revolutionizing patient care management by integrating artificial intelligence and machine learning technologies. This comprehensive article examines the implementation, challenges, and outcomes of predictive analytics across healthcare facilities worldwide. The article explores diverse data sources, including electronic health records (EHRs), wearable technology, insurance claims, genomic information, and patient-reported outcomes, highlighting their role in improving clinical decision-making. Advanced analy

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Vaghani, Divyeshkumar. "Predictive Analysis for Personalized Machine: Leveraging Patient Data for Enhanced Healthcare." International Journal of Current Science Research and Review 07, no. 05 (2024): 2972–87. https://doi.org/10.5281/zenodo.11258171.

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Abstract : This research explores predictive analysis for personalized machine: leveraging patient data for enhanced healthcare. By leveraging the power of information and analytics, the healthcare industry can be driven towards a more patient-centric, proactive model that enhances outcomes and improve the overall quality of care. The objectives of the study are to: determine the significance and challenges of predictive analytics in healthcare, ascertain the data analytics techniques used in healthcare to enhance patient care, find out how predictive analytics can be applied for enhanced heal

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Liu, Vincent X., David W. Bates, Jenna Wiens, and Nigam H. Shah. "The number needed to benefit: estimating the value of predictive analytics in healthcare." Journal of the American Medical Informatics Association 26, no. 12 (2019): 1655–59. http://dx.doi.org/10.1093/jamia/ocz088.

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Abstract Predictive analytics in health care has generated increasing enthusiasm recently, as reflected in a rapidly growing body of predictive models reported in literature and in real-time embedded models using electronic health record data. However, estimating the benefit of applying any single model to a specific clinical problem remains challenging today. Developing a shared framework for estimating model value is therefore critical to facilitate the effective, safe, and sustainable use of predictive tools into the future. We highlight key concepts within the prediction-action dyad that t

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Sail1, Paresh V. "Big Data in Healthcare – A Comprehensive Review." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50942.

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Big Data has revolutionized the healthcare industry by enabling the collection, storage, and analysis of vast amounts of medical information from diverse sources such as electronic health records (EHRs), medical imaging, wearable devices, genomic data, and real-time patient monitoring. The integration of advanced analytics, artificial intelligence (AI), and machine learning (ML) has significantly enhanced clinical decision-making, disease prediction, personalized treatment, and public health management. This paper explores the transformative role of Big Data in healthcare, focusing on its key

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Ahmad Sukri, Nur Farah Afifah, Wan Mohd Amir Fazamin Wan Hamzah, Mohd Kamir Yusof, Ismahafezi Ismail, Harmy Mohamed Yusoff, and Azliza Yacob. "A Systematic Literature Review on Machine Learning in Healthcare Prediction." International Journal of Online and Biomedical Engineering (iJOE) 21, no. 06 (2025): 155–77. https://doi.org/10.3991/ijoe.v21i06.54211.

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Rapid technological advancement will continue to create new values and transform experiences in many sectors, including healthcare. Several key trends are shaping today’s healthcare system, including the use of machine learning (ML). This systematic literature review (SLR) explores the application of ML in healthcare, particularly in predictive analytics. The SLR also includes a few papers on machine learning operations (MLOps) in healthcare, reflecting limited studies on the topic. This suggests significant potential for further exploration in MLOps. The review compares findings from various

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Fabio, G., and M. Carrabba. "Healthcare-Associated Pneumonia and Predictive Scores." Clinical Infectious Diseases 56, no. 8 (2013): 1187–88. http://dx.doi.org/10.1093/cid/cis1221.

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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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Chowdhury, Anupam, and Costas D. Maranas. "Personalized Kinetic Models for Predictive Healthcare." Cell Systems 1, no. 4 (2015): 250–51. http://dx.doi.org/10.1016/j.cels.2015.10.008.

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Rane, Milind, Megha Derkar, Devansh Kabra, and Tanuja Desai. "Chronic Kidney Disease Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 65–70. http://dx.doi.org/10.22214/ijraset.2024.62593.

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Abstract: Chronic kidney disease (CKD) is a progressive condition in which the kidneys lose their ability to function effectively over time. Individuals with hypertension, diabetes, or a family history of CKD are at increased risk, emphasizing the importance of early detection for effective intervention and management. Recent research has focused on employing machine learning techniques, including Ant Colony Optimization (ACO) and Support Vector Machine (SVM) classifiers, to predict CKD presence using a minimal set of features. This study aims to optimize predictive accuracy through advanced m

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Sumathi, P., Arun Kumar S, and Balaji A. "Healthcare - Autism Predicting Tool Using Data Science / AI / ML." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 440–43. http://dx.doi.org/10.22214/ijraset.2024.60421.

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Abstract: This study presents a comprehensive analysis of the application of machine learning techniques for the prediction of autism spectrum disorder (ASD). The dataset used in this research comprises a range of demographic, behavioral, and diagnostic features. The study focuses on the use of various machine learning algorithms, including limited decision trees, support vector machines, and neural networks, to predict the likelihood of ASD in individuals. In addition, engineering and feature selection strategies are investigated to determine the most pertinent characteristics for precise pre

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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 invlove

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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

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Rahim, Md Jawadur, Ahlina Afroz, and Omolola Akinola. "Predictive Analytics in Healthcare: Big Data, Better Decisions." International Journal of Scientific Research and Modern Technology 4, no. 1 (2025): 1–21. https://doi.org/10.5281/zenodo.14630840.

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The healthcare systems worldwide are moving towards the concept of predictive analytics, using data on patients for better and effective treatment and to organize usage of resources effectively. Given the exponential growth in digitalization and electronic health records (EHRs), machine learning (ML) and big data analytical models present the greatest forms of predictive health care. Hence, this comprehensive review will endeavor to make an evidence based, up-to-data compilation of past, current and future findings on data analytics applications in the domain of predictive healthcare. Material

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Alghamdi, A., T. Alsubait, A. Baz, and H. Alhakami. "Healthcare Analytics: A Comprehensive Review." Engineering, Technology & Applied Science Research 11, no. 1 (2021): 6650–55. https://doi.org/10.48084/etasr.3965.

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Big data have attracted significant attention in recent years, as their hidden potentials that can improve human life, especially when applied in healthcare. Big data is a reasonable collection of useful information allowing new breakthroughs or understandings. This paper reviews the use and effectiveness of data analytics in healthcare, examining secondary data sources such as books, journals, and other reputable publications between 2000 and 2020, utilizing a very strict strategy in keywords. Large scale data have been proven of great importance in healthcare, and therefore there is a need f

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