Explainable Machine Learning for Cardiovascular Disease Risk Stratification Using Electronic Health Records: An XGBoost–SHAP Framework

Cardiovascular disease (CVD) remains one of the most critical public health challenges worldwide, requiring early identification of high-risk patients to reduce adverse outcomes such as myocardial infarction, stroke, heart failure, and cardiovascular-related mortality. Traditional risk prediction tools, including rule-based scoring systems, provide valuable clinical guidance but often have limited ability to capture complex nonlinear relationships among heterogeneous patient variables stored in Electronic Health Records (EHRs).