Key result
A gradient boosting decision tree machine learning model using downsampling and 24 clinical features achieved an F1 score of 0.83, accuracy of 0.83, and an AUC of 0.91 for predicting in-hospital myocardial infarction.
Why the study?
Traditional myocardial infarction risk models are subjective, require hard-to-obtain data, and typically focus only on high-risk groups rather than continuous active monitoring of inpatients in noncardiovascular departments.
Observational (n=234,815)
No
An artificial intelligence-based machine learning model utilizing gradient boosting decision trees can accurately predict in-hospital myocardial infarction risk using routine electronic medical record and laboratory data.
AI MI prediction model supports real-time inpatient monitoring; leaves open prospective validation before clinical adoption.
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 both 0.83, and the area under the curve was 0.91. CONCLUSION: Compared with traditional models, artificial intelligence-based machine learning models have better accuracy and real-time performance and can reduce the occurrence of in-hospital MI from a data-driven perspective, thereby increasing the cure rate of patients and improving their prognosis.
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Liu et al. (2022) conducted an observational in Myocardial infarction (n=234,815). Artificial intelligence-based risk prediction model (GBDT) vs. Other machine learning algorithms and traditional models was evaluated on Model predictive performance (F1 score, accuracy, and AUC) on the test set. A gradient boosting decision tree machine learning model using downsampling and 24 clinical features achieved an F1 score of 0.83, accuracy of 0.83, and an AUC of 0.91 for predicting in-hospital myocardial infarction.
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