Comparison of machine-learning models for the prediction of 1-year adverse outcomes of patients undergoing primary percutaneous coronary intervention for acute ST-elevation myocardial infarction.

ML-based models, such as DRF and GBM, can effectively identify high-risk STEMI patients for adverse events during follow-up. These models can be useful for personalized treatment strategies, ultimately improving clinical outcomes and reducing the burden of disease.