Predicting the risk of mechanical complications in acute myocardial infarction using an interpretable machine learning model

Background Mechanical complications in acute myocardial infarction (AMI) progress rapidly and carry very high mortality, underscoring the need for early risk prediction. Existing studies use a narrow range of models with poorly characterized decision-making. This study aimed to develop and validate an interpretable prediction model for post-AMI mechanical complications to guide individualized treatment and optimize resource allocation. Methods A total of 850 patients with and without mechanical complications enrolled in this study. 58 features were selected for model training and validation. Eight machine learning algorithms were used to build prediction models, whose performance was assessed AUC, accuracy, F1 score and other indicators. The SHAP method was applied to rank feature importance and interpret the final model. Results Among the eight machine learning models, LightGBM showed the best discriminative performance. After feature reduction, a 13-variable interpretable LightGBM model was established, achieving excellent discrimination for mechanical complications in the validation set (AUC = 0.9587). SHAP analysis identified age, hsCRP (High-Sensitivity C-Reactive Protein), drinking history, D-dimer, sex, NEUT (Neutrophils), PCI (Percutaneous Coronary Intervention), MPV (Mean Platelet Volume), history of chronic lung disease, SGLT2i (Sodium-Glucose Cotransporter 2 Inhibitors) use, LDH (Lactate Dehydrogenase), AMI category, and MA (Malignant Arrhythmia) as the most influential predictors. Conclusions The interpretable ML model provides both global and patient-level explanations, and the simplified key-feature model is suitable for deployment as a clinical decision-support tool for rapid screening and risk assessment in emergency settings.