Using Machine Learning to Predict the In-Hospital Mortality in Women with ST-Segment Elevation Myocardial Infarction.

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.