Heart Attack Risk Prediction Using Machine Learning and SHAP Explainability

Heart disease remains one of the leading causes of mortality worldwide. Early detection of heartattack risk is crucial for timely intervention. In this study, we employ Random Forest and XGBoostmodels for predictive analysis of patient health data. In addition, SHAP explainability enhancesthe interpretation of the model, identifying key risk factors such as cholesterol levels, age, andblood pressure. Our final XGBoost model achieves 87% accuracy, demonstrating reliable predictivecapabilities.