machine learning with shap-driven interpretability enhances decision-making in coronary bifurcation percutaneous coronary intervention: a prospective study

Background: this prospective registry-based cross-sectional study of 500 percutaneous coronary intervention (pci) patients assessed lesion morphology, clinical and procedural determinants of coronary bifurcation complexity, and the utility of machine learning (ml) for lesion stratification.objectives: characterize bifurcation lesion classes (0: no bifurcation; 1: simple; 2: complex), identify key demographic, anatomical, and procedural predictors of complexity, and evaluate interpretable ml models for accurate classification.methods: we analyzed patient demographics, comorbidities, angiographic features (e.g., side-branch stenosis, bifurcation angle, calcification), and procedural outcomes, selecting ten critical complexity drivers. four ml approaches — k-nearest neighbors (knn), support vector machines (svm), ensemble trees, and probabilistic classifiers — were optimized via hyperparameter tuning and feature-selection methods, with shapley additive explanations (shap) values quantifying feature importance.results: the shap analysis identified side-branch stenosis, heavy calcification, and dual-stent technique as top predictors. weighted knn and medium-scale svm achieved 89 - 92% accuracy, while ensemble models peaked at 97.8% using 10 - 15 features. complex lesions (class 2) required dual-stent deployment more often (35% vs. 10%) and had lower post-pci timi 3 flow (85% vs. 92%). wrapper-based feature selection outperformed filter and embedded methods, reaching 96.8% accuracy.conclusions: integrating anatomical metrics, patient risk factors, and interpretable ml significantly improves pci decision-making for bifurcation lesions, outperforming traditional systems and enabling personalized interventional strategies to optimize outcomes and resource allocation.