An artificial intelligence-based risk prediction model of myocardial infarction

作者：Ran Liu, Miye Wang, Tao Zheng, Rui Zhang, Nan Li, Zhongxiu Chen, Hongmei Yan, Qingke Shi · 发表于：BMC Bioinformatics · 年份：2022 · DOI：10.1186/s12859-022-04761-4 · 被引用次数：42 · 研究领域：Artificial Intelligence in Healthcare、Acute Myocardial Infarction Research、ECG Monitoring and Analysis

BACKGROUND: Myocardial infarction can lead to malignant arrhythmia, heart failure, and sudden death. Clinical studies have shown that early identification of and timely intervention for acute MI can significantly reduce mortality. The traditional MI risk assessment models are subjective, and the data that go into them are difficult to obtain. Generally, the assessment is only conducted among high-risk patient groups. OBJECTIVE: To construct an artificial intelligence-based risk prediction model of myocardial infarction (MI) for continuous and active monitoring of inpatients, especially those in noncardiovascular departments, and early warning of MI. METHODS: The imbalanced data contain 59 features, which were constructed into a specific dataset through proportional division, upsampling, downsampling, easy ensemble, and w-easy ensemble. Then, the dataset was traversed using supervised machine learning, with recursive feature elimination as the top-layer algorithm and random forest, gradient boosting decision tree (GBDT), logistic regression, and support vector machine as the bottom-layer algorithms, to select the best model out of many through a variety of evaluation indices. RESULTS: GBDT was the best bottom-layer algorithm, and downsampling was the best dataset construction method. In the validation set, the F1 score and accuracy of the 24-feature downsampling GBDT model were both 0.84. In the test set, the F1 score and accuracy of the 24-feature downsampling GBDT model were...