Interpretable Machine Learning for Coronary Artery Disease Risk Stratification: A SHAP-Based Analysis
Tasmurzayev N. Baigarayeva Z. Amangeldy B. Imanbek B. Kurmanbek S. Dikhanbayeva G. Amirkhanova G.
November 2025Multidisciplinary Digital Publishing Institute (MDPI)
Algorithms
2025#18Issue 11
Coronary artery disease (CAD) is a leading cause of global mortality, demanding accurate and early risk assessment. While machine learning models offer strong predictive power, their clinical adoption is often hindered by a lack of transparency and reliability. This study aimed to develop and rigorously evaluate a calibrated, interpretable machine learning framework for CAD prediction using 56 routinely collected clinical and demographic variables from the Z-Alizadeh Sani dataset (n = 303). A systematic protocol involving comprehensive preprocessing, class rebalancing using SMOTE, and grid-search hyperparameter tuning was applied to five distinct classifiers. The XGBoost model demonstrated the highest predictive performance, achieving an accuracy of 0.9011, an F1 score of 0.8163, and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.92. Post hoc interpretability analysis using SHAP (Shapley Additive Explanations) identified HTN, valvular heart disease (VHD), and diabetes mellitus (DM) as the most significant predictors of CAD. Furthermore, calibration analysis confirmed that the mode’s probability estimates are reliable for clinical risk stratification. This work presents a robust framework that combines high predictive accuracy with clinical interpretability, offering a promising tool for early CAD screening and decision support.
clinical risk factors , coronary artery disease , CVD , explainable AI , machine learning , risk prediction , SHAP , SMOTE
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Faculty of Information Technologies and Artificial Intelligence, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
LLP Kazakhstan R&D Solutions Co., Ltd., Almaty, 050056, Kazakhstan
Faculty of Postgraduate Higher Medical Education, Akhmet Yasawi University, Shymkent, 161200, Kazakhstan
Faculty of Information Technologies and Artificial Intelligence
LLP Kazakhstan R&D Solutions Co.
Faculty of Postgraduate Higher Medical Education
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