Comparing machine learning models and traditional approaches for predicting obstructive coronary artery disease: a systematic review and meta-analysis.

BACKGROUND: Obstructive coronary artery disease (oCAD) is a major cause of cardiovascular morbidity and mortality, and accurate prediction is essential for guiding clinical decision-making and secondary prevention. However, the potential of machine learning (ML) models for predicting oCAD remains insufficiently explored; therefore, this study aimed to compare the performance of ML methods with traditional approaches and evaluate differences among ML algorithm classes. METHODS: A systematic search of EMBASE, Web of Science, Cochrane Library, Scopus, and PubMed was conducted from inception to February 9, 2026. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool. Random-effects meta-analysis was performed to pool AUCs of ML algorithms and compare the predictive performance of ML models with traditional approaches for oCAD. Meta-regression and subgroup analyses were used to explore heterogeneity. RESULTS: A total of 41 studies were included in the review and meta-analysis. ML models showed significantly higher predictive performance than traditional approaches in 12 studies, although with substantial heterogeneity (MD: 0.09, 95% CI: 0.05-0.13; p < 0.00001; I 2 = 95%). Heterogeneity in AUC was mainly associated with differences in diagnostic methods and predictors for oCAD. Among the algorithms evaluated, LR, RF, and XGBoost were the most commonly employed, suggesting their potential in clinical practice. CONCLUSIONS: ML shows promising discrimination relative to traditional scores. Future studies should emphasize adequate sample-size calculation, appropriate feature-selection strategies, and standardized handling of missing data and focus on developing and validating ML models based on accessible and noninvasive data sources.