Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model

作者：Qingqing Lin, Wenxiang Zhao, Hailin Zhang, Wenhao Chen, Sheng Lian, Qinyun Ruan, Zhaoyang Qu, Yi-min Lin, Dajun Chai, Xiaoyan Lin · 发表于：Frontiers in Cardiovascular Medicine · 年份：2025 · DOI：10.3389/fcvm.2025.1444323 · 被引用次数：11 · 研究领域：Artificial Intelligence in Healthcare、Acute Myocardial Infarction Research、Machine Learning in Healthcare

Background: Early prediction of heart failure (HF) after acute myocardial infarction (AMI) is essential for personalized treatment. We aimed to use interpretable machine learning (ML) methods to develop a risk prediction model for HF in AMI patients. Methods: We retrospectively included patients initially with AMI who received percutaneous coronary intervention (PCI) in our hospital from November 2016 to February 2020. The primary endpoint was the occurrence of HF within 3 years after operation. For developing a predictive model for HF risk in AMI patients, the least absolute shrinkage and selection operator (LASSO) Regression was used to feature selection, and four ML algorithms including Random Forest (RF), Extreme Gradient Boost (XGBoost), Support Vector Machine (SVM), and Logistic Regression (LR) were employed to develop the model on the training set. The performance evaluation of the prediction model was carried out on the training set and the testing set, utilizing metrics including AUC (Area under the receiver operating characteristic curve), calibration plot, and decision curve analysis (DCA). In addition, we used the Shapley Additive Explanations (SHAP) value to determine the importance of the selected features and interpret the optimal model. Results: A total of 1220 AMI patients were included and 244 (20%) patients developed HF during follow-up. Among the four evaluated ML models, the XGBoost model exhibited exceptional accuracy, with an AUC value of 0.922. The SHA...