A Review of Predictive Analytics in Managing Chronic Diseases in Aging Populations

ABSTRACT

This review paper explores the role of predictive analytics in managing chronic diseases among aging populations, a demographic increasingly burdened by complex health challenges. As healthcare systems strive to enhance patient outcomes, predictive analytics offers powerful tools to identify at-risk individuals, forecast disease progression, and personalize treatment strategies. This paper examines the current applications of predictive models in managing chronic conditions such as diabetes, cardiovascular diseases, and Alzheimer’s, highlighting the advantages of early intervention and proactive care. The review addresses key challenges, including interoperability issues, data privacy concerns, algorithmic bias, and resistance to change among healthcare providers. Ethical considerations surrounding the use of predictive analytics are discussed, emphasizing the importance of transparency and patient engagement. The paper concludes with recommendations for improving the integration of predictive analytics in chronic disease management, including investments in technology infrastructure, enhanced education for healthcare professionals, and ongoing monitoring of predictive models. By effectively leveraging predictive analytics, healthcare systems can move toward a more personalized and preventive approach, ultimately improving the health and quality of life for aging populations facing chronic diseases.

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World Scientific News 204 (2025) 170-170

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WSN 204 (2025) 159-170