Application of Machine Learning Algorithms in Predicting Major Adverse Cardiovascular Events after Percutaneous Coronary Intervention in Patients with New-Onset ST-Segment Elevation Myocardial Infarction.

Abstract

Background: This study aimed to develop and validate a predictive model for major adverse cardiovascular events (MACE) following percutaneous coronary intervention (PCI) in patients with new-onset ST-segment elevation myocardial infarction (STEMI) using four machine learning (ML) algorithms.

Methods: Data from 250 new-onset STEMI patients were retrospectively collected. Feature selection was performed using the Boruta algorithm. Four ML algorithms-K-nearest neighbors (KNN), support vector machine (SVM), Complement Naive Bayes (CNB), and logistic regression-were applied to predict MACE risk. Model performance was evaluated using area under the curve (AUC), sensitivity, and specificity. Shapley Additive Explanations (SHAP) analysis was used to rank feature importance, and a nomogram was constructed for risk visualization.

Results: Logistic regression showed the best performance (AUC = 0.814 in training, 0.776 in validation) compared to KNN, SVM, and CNB. SHAP analysis identified seven key predictors, including Killip classification, Gensini score, blood urea nitrogen (BUN), heart rate (HR), creatinine (CR), glutamine transferase (GLT), and platelet count (PCT). The nomogram provided accurate risk predictions with strong agreement between predicted and observed outcomes.

Conclusions: The logistic regression model effectively predicts MACE risk after PCI in STEMI patients. The nomogram serves as a practical tool for clinicians, supporting personalized risk assessment and improving clinical decision-making.