Machine learning model using routine clinical indicators predicts coronary heart disease riskMachine learning model predicts heart disease risk with high accuracy

BackgroundTo develop and externally validate a coronary heart disease (CHD) risk model from routine clinical indicators and identify key predictors.MethodsThe Framingham Heart Study cohort (n = 4,240) was used. Missing values and outliers were handled, and class imbalance was corrected with SMOTEENN/SMOTETomek. Data were split 7:3 for training and internal validation. A two-tier feature selection (chi-square, mutual information, ANOVA F-test) retained ten variables. A stacked ensemble of gradient boosting, random forest, and XGBoost with a logistic-regression meta-learner was trained. Performance was measured by AUC, accuracy, precision, recall, and F1. External validation used a retrospective hospital cohort (n = 200; 2024–2025). Model explanations were derived with SHAP.ResultsInternal validation yielded AUC 0.977 and accuracy 0.942 (F1: 0.944). External validation achieved AUC 0.929 and accuracy 0.885. SHAP identified systolic blood pressure, age, total cholesterol, and fasting glucose as leading contributors, with plausible nonlinear effects and interactions.ConclusionA model built from routinely available measures demonstrates strong discrimination for CHD risk and generalizes to an external cohort, offering a clinically interpretable tool for cardiovascular risk assessment.