ArticleFrontiers in cardiovascular medicine2026
Development and validation of a machine learning model for predicting coronary artery disease risk: a retrospective study.
Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: The mortality rate from coronary atherosclerotic heart disease is rising year by year, affecting an increasingly younger population, and is projected to continue rising in the future. Although medical technology is advancing, coronary angiography cannot be widely adopted for screening due to factors such as economic constraints, disparities in regional healthcare standards, and patient compliance. This study aimed to develop and validate a machine learning-based predictive model for assessing the severity of coronary artery lesions and to explore related risk factors. Methods: This retrospective study included 400 patients who underwent their first complete coronary angiography at the First Affiliated Hospital of Hainan Medical University between 2023 and 2025. The patient dataset from July 2023 to July 2025 ( Results: Ten predictors were retained. The XGBoost model exhibited the best discrimination, with AUCs of 0.889 (95% CI: 0.819-0.945) in the internal test set and 0.803 (95% CI: 0.718-0.888) in temporal validation. The model also showed good calibration and low Brier scores, with stable performance in ten-fold cross-validation. DeLong's test confirmed its superiority over competing models. DCA and SHAP analysis further supported its clinical utility and interpretability. Conclusion: This study developed a machine learning model for predicting the severity of coronary lesions, demonstrating satisfactory performance in a single-center setting.
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