A Machine Learning Framework for Predictive Health Risk Assessment and Early Intervention

Improving healthcare outcomes and facilitating prompt treatments depend on the early identification of high-risk individuals. This paper presents a comprehensive machine learning framework for predictive health risk assessment by integrating clinical, demographic, and lifestyle data from multiple sources. The proposed framework includes systematic data preprocessing, feature selection, and model development using ensemble and deep learning techniques to ensure accurate and reliable predictions. Explainable Artificial Intelligence (XAI) methods are incorporated to provide interpretable insights into critical risk factors, thereby supporting clinical decision-making. The framework is evaluated on benchmark healthcare datasets for predicting chronic diseases such as diabetes and cardiovascular disorders. Experimental results demonstrate that the proposed approach outperforms conventional statistical models in terms of accuracy, precision, recall, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The findings highlight the potential of machine learning to transform predictive healthcare by enabling early intervention and personalized treatment strategies.