IMR Press / RCM / Volume 24 / Issue 5 / DOI: 10.31083/j.rcm2405126
Open Access Original Research
Using Machine Learning to Predict the In-Hospital Mortality in Women with ST-Segment Elevation Myocardial Infarction
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1 Department of Communication Engineering, School of Electrical and Information Engineering, Tianjin University, 300072 Tianjin, China
2 Department of Emergency, Thoracic Clinical College, Tianjin Medical University, 300070 Tianjin, China
3 Department of Emergency, Tianjin Jinnan Hospital, 300350 Tianjin, China
4 Department of Cardiology, Tianjin Chest Hospital, 300222 Tianjin, China
*Correspondence: orange2012@126.com (Jia Zhao); sunguolei1020@163.com (Guolei Sun); zhoujiawenzhang@126.com (Jia Zhou)
These authors contributed equally.
Rev. Cardiovasc. Med. 2023, 24(5), 126; https://doi.org/10.31083/j.rcm2405126
Submitted: 20 October 2022 | Revised: 28 January 2023 | Accepted: 3 February 2023 | Published: 24 April 2023
(This article belongs to the Special Issue Risk Stratification in Cardiovascular Diseases)
Copyright: © 2023 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.
Abstract

Background: Several studies have shown that women have a higher mortality rate than do men from ST-segment elevation myocardial infarction (STEMI). The present study was aimed at developing a new risk-prediction model for all-cause in-hospital mortality in women with STEMI, using predictors that can be obtained at the time of initial evaluation. Methods: We enrolled 8158 patients who were admitted with STEMI to the Tianjin Chest Hospital and divided them into two groups according to hospital outcomes. The patient data were randomly split into a training set (75%) and a testing set (25%), and the training set was preprocessed by adaptive synthetic (ADASYN) sampling. Four commonly used machine-learning (ML) algorithms were selected for the development of models; the models were optimized by 10-fold cross-validation and grid search. The performance of all-population-derived models and female-specific models in predicting in-hospital mortality in women with STEMI was compared by several metrics, including accuracy, specificity, sensitivity, G-mean, and area under the curve (AUC). Finally, the SHapley Additive exPlanations (SHAP) value was applied to explain the models. Results: The performance of models was significantly improved by ADASYN. In the overall population, the support vector machine (SVM) combined with ADASYN achieved the best performance. However, it performed poorly in women with STEMI. Conversely, the proposed female-specific models performed well in women with STEMI, and the best performing model achieved 72.25% accuracy, 82.14% sensitivity, 71.69% specificity, 76.74% G-mean and 79.26% AUC. The accuracy and G-mean of the female-specific model were greater than the all-population-derived model by 34.64% and 9.07%, respectively. Conclusions: A machine-learning-based female-specific model can conveniently and effectively identify high-risk female STEMI patients who often suffer from an incorrect or delayed management.

Keywords
in-hospital mortality
machine learning
prediction model
SHAP value
STEMI
women
Funding
62206197/National Natural Science Foundation of China
21JCYBJC00820/Applied and Basic Research by Multi-input Foundation of Tianjin
TJWJ2022QN067/Tianjin Health Research Project
2022001/Tianjin Key Research Program of Traditional Chinese Medicine
2023006/Tianjin Key Research Program of Traditional Chinese Medicine
20220108/Committee on Science and Technology, Jinnan District, Tianjin
Figures
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