A comprehensive study on prevention, early diagnosis, and management of chronic diseases

Abstract

Background: Chronic illnesses — cardiovascular disease, type 2 diabetes, chronic respiratory disease, and cancer — account for over 74% of global mortality and burden healthcare systems worldwide.

Objective: To evaluate ML, AI, and digital health technologies for chronic disease prevention, early diagnosis, and management, and to develop a hybrid explainable AI (XAI) framework for risk stratification.

Methods: Evidence was synthesized from epidemiological studies, electronic health records, and multicenter trials involving over 50,000 patients across diverse demographics. A hybrid framework combining deep neural networks, ensemble learning, and XAI techniques was developed for multi-disease risk prediction.

Results: The framework achieved 94.7% diagnostic accuracy, 93.5% sensitivity, 95.6% specificity, and AUC-ROC of 0.97 across multiple diseases. Ensemble models outperformed individual algorithms in stability. Disease prevalence and risk indicators varied by demographic subgroup, supporting personalized intervention. Lifestyle, behavioral, and community-based preventive strategies reduced disease occurrence by up to 40%. Wearable biosensors, continuous glucose monitoring, mental health co-management, and personalized pharmacotherapy were also examined.

Conclusion: Explainable AI and digital health integration enable accurate, personalized chronic disease risk stratification and early detection, offering practical guidance for clinicians, policymakers, and AI developers.