A Machine Learning–Driven Health Risk Index for Predicting Chronic Disease Burden

The rising global burden of chronic diseases necessitates proactive, data-driven approaches for early risk identification and intervention. This study proposes a Machine Learning–Driven Health Risk Index (ML-HRI) designed to predict individual susceptibility to chronic conditions such as cardiovascular disease, diabetes, and hypertension. The model integrates heterogeneous health data, including demographic attributes, clinical indicators, lifestyle factors, and behavioral patterns, to generate a composite risk score. Multiple machine learning algorithms, such as random forests, gradient boosting, and logistic regression, are evaluated to optimize predictive performance, with feature selection techniques employed to enhance interpretability and reduce dimensionality. The proposed ML-HRI is validated using a real-world dataset, demonstrating improved accuracy and sensitivity compared to traditional risk assessment methods. The framework emphasizes scalability and adaptability, enabling continuous learning as new data becomes available. Additionally, explainable AI techniques are incorporated to provide transparency in risk predictions, facilitating clinical trust and usability. The results indicate that the ML-HRI can effectively stratify populations into distinct risk categories, supporting targeted prevention strategies and resource allocation in healthcare systems. This approach advances personalized medicine by offering a robust, data-centric tool for chronic disease risk management.