Artificial Intelligence–Driven Population Health Surveillance Framework for Early Prediction of Cardiometabolic and Age-Related Complications in Diabetic Patients

Abstract

Diabetes mellitus has emerged as one of the leading global public health challenges, significantly increasing the risk of cardiometabolic disorders, cardiovascular diseases, and age-related health complications among elderly populations. Early prediction and continuous surveillance of these complications remain critical for reducing mortality, hospitalization rates, and healthcare burdens. This study proposes an AI–driven population health surveillance framework for the early prediction of cardiometabolic and age-related complications in diabetic patients using intelligent predictive analytics and machine learning techniques. The proposed framework integrates heterogeneous healthcare datasets, including electronic health records, clinical biomarkers, demographic information, lifestyle parameters, cardiovascular indicators, and longitudinal patient monitoring data to enhance predictive healthcare intelligence. The framework employs advanced AI models, including Random Forest, XGBoost, Long Short-Term Memory, and ensemble deep learning architectures, for automated risk stratification and complication prediction. Data preprocessing techniques such as missing value imputation, normalization, feature engineering, and dimensionality reduction are incorporated to improve model robustness and prediction stability. Explainable Artificial Intelligence mechanisms are additionally integrated to improve interpretability and clinical decision support for healthcare professionals. Experimental evaluation demonstrates that the proposed AI-driven surveillance framework achieves superior predictive performance compared with conventional statistical healthcare models. The hybrid AI model achieved an accuracy of 97.2%, precision of 96.4%, recall of 95.9%, F1-score of 96.1%, and area under the ROC curve (AUC) of 98.1% for early prediction of cardiometabolic and age-related complications in diabetic patients. Furthermore, the proposed framework significantly improved early risk identification, disease progression monitoring, and population-level healthcare surveillance efficiency. The findings indicate that AI-enabled predictive healthcare systems can substantially support intelligent clinical decision-making, personalized treatment planning, and proactive disease prevention strategies for diabetic populations, ultimately contributing toward sustainable and data-driven healthcare management systems.