Predictive Analytics Model for Chronic Disease Management Using Integrated Population Health Data

Sylviastella Favour Peteranaba; Helen Ekwi Osinem; Florence Eribenne; Olakunle; Saheed Soyege

doi:10.56201/ijhpr.v8.no4.2023.pg205.230

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Predictive Analytics Model for Chronic Disease Management Using Integrated Population Health Data

Sylviastella Favour Peteranaba, Helen Ekwi Osinem, Florence Eribenne, Olakunle, Saheed Soyege

Abstract

This study proposes a predictive analytics model for chronic disease management using integrated population health data to enhance early risk identification, personalized interventions, and health system efficiency. Chronic diseases such as diabetes, cardiovascular diseases, respiratory disorders, and hypertension account for a substantial proportion of global morbidity, mortality, and healthcare expenditure, particularly in resource-constrained and fragmented health systems. The proposed model integrates heterogeneous population health datasets, including electronic health records, demographic profiles, lifestyle indicators, environmental exposure data, insurance claims, and community-level social determinants of health. By leveraging advanced machine learning techniquessuch as gradient boosting, random forests, and deep neural networksthe model is designed to identify high-risk individuals, predict disease progression trajectories, and support proactive clinical and public health decision- making.A population-centric architecture is adopted, emphasizing interoperability, data quality assurance, and privacy-preserving analytics. Feature engineering processes are applied to capture temporal patterns, comorbidity interactions, and behavioral risk factors that are often underrepresented in traditional clinical models. The predictive framework is validated using historical longitudinal data and evaluated through performance metrics including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve. Results demonstrate improved predictive performance and earlier detection of adverse health outcomes compared to conventional rule-based and single-source predictive approaches.Beyond clinical prediction, the model supports stratified care planning, targeted preventive interventions, and optimized resource allocation across healthcare systems. It enables policymakers and healthcare administrators to identify population-level risk clusters, anticipate service demand, and design evidence-based chronic disease prevention strategies. The study also highlights ethical and governance considerations, including data security, algorithmic transparency, and bias mitigation, to ensure responsible deployment in real-world settings.Overall, the proposed predictive analytics model advances chronic disease management by integrating multidimensional population health data into a scalable, data-driven framework. Its application has the potential to improve patient outcomes, reduce avoidable hospitalizations, and strengthen the sustainability of population health management initiatives in diverse healthcare contexts. Future research should explore real-time data integration, cross-jurisdictional scalability, and clinician-in-the-loop validation mechanisms to enhance model trust, adoption, and long-term impact across primary, secondary, and community-based healthcare delivery systems worldwide. This approach aligns with global digital health transformation agendas worldwide.

Keywords

Predictive Analytics; Chronic Disease Management; Population Health Data; Machine Learning; Health Informatics; Risk Stratification; Public Health Analytics.

References

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Predictive Analytics Model for Chronic Disease Management Using Integrated Population Health Data

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