en General پژوهشي Research Article <div style="text-align: justify;"><span style="font-size:14px;"><span style="font-family:Times New Roman;"><strong>Background:</strong> 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.<br> <strong>Objectives:</strong> 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.<br> <strong>Methods: </strong>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 &mdash; k-nearest neighbors (KNN), support vector machines (SVM), ensemble trees, and probabilistic classifiers &mdash; were optimized via hyperparameter tuning and feature-selection methods, with SHapley Additive exPlanations (SHAP) values quantifying feature importance.<br> <strong>Results:</strong> 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.<br> <strong>Conclusions: </strong>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.</span></span></div> Coronary Bifurcation,Machine Learning,SHAP Analysis,Percutaneous Coronary Intervention,Lesion Complexity,Procedural Outcomes 0 0 http://mcj.bums.ac.ir/browse.php?a_code=A-10-1-322&slc_lang=en&sid=1 Alireza Khosravi 10031947532846004857 10031947532846004857 No Cardiology Hypertension Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran Iman Zand 10031947532846004858 0009-0003-8455-7382 No Department of Cardiology, Interventional Cardiology Research Center, Cardiovascular Research Institute, Chamran Hospital, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran Ehsan Shirvani 10031947532846004859 10031947532846004859 No Interventional Cardiology Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Ira Bashir Najafabadian 10031947532846004860 0009-0001-1395-8694 No Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Mohaddeseh Behjati 10031947532846004861 10031947532846004861 Yes Interventional Cardiology Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran. Email: dr.mohaddesehbehjati@gmail.com