eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2026-03-20 19 1 348 356 10.13005/bpj/3357 70006 article Bhupesh Rawat 1 Himanshu Pant 2 Ankur Bist 3 Department of School of Computing, Graphic Era Hill University, Bhimtal, India Department of Centre for Promotion of Research, Graphic Era (Deemed to be) University, Dehradun, Uttarakhand, India Department of Computer Science and Engineering (CSE), Graphic Era Hill University, Bhimtal, India Cardiovascular disease remains a leading cause of global mortality, underscoring the urgent need for accurate and interpretable risk prediction tools. This study presents a machine learning framework combining predictive modeling with SHapley Additive exPlanations (SHAP) to identify clinically actionable risk factors for coronary artery disease using the Cleveland Heart Disease dataset. We evaluated three models—Logistic Regression (baseline), Random Forest, and XGBoost—with SHAP-based interpretability to bridge the gap between model performance and clinical utility. Our results demonstrate that XGBoost achieved superior predictive accuracy (88.2% accuracy, AUC(Area under curve=0.91), while SHAP analysis revealed maximum heart rate during exercise (thalach) and ST depression magnitude as the most significant modifiable risk factors, alongside non-modifiable determinants like age and sex. The framework provides physicians with both risk scores and interpretable decision pathways, offering a template for deploying explainable artificial intelligence in preventive cardiology. Key findings highlight the potential of SHAP values to align machine learning outputs with clinical priorities, emphasizing factors amenable to intervention while maintaining diagnostic transparency. https://biomedpharmajournal.org/vol19no1/interpretable-machine-learning-for-heart-disease-risk-assessment-leveraging-shap-values-to-identify-clinically-actionable-predictors/ Cardiovascular Risk Prediction Clinical Decision Support Cleveland Dataset Explainable AI Interpretable Machine Learning SHAP(Shapley Additive Explanations) Values