Limited research has been conducted in Asian elderly patients (aged 65 years and above) for in-hospital mortality prediction after an ST-segment elevation myocardial infarction (STEMI) using Deep Learning (DL) and Machine Learning (ML). We used DL and ML to predict in-hospital mortality in Asian elderly STEMI patients and compared it to a conventional risk score for myocardial infraction outcomes. Malaysia's National Cardiovascular Disease Registry comprises an ethnically diverse Asian elderly population (3991 patients). 50 variables helped in establishing the in-hospital death prediction model. The TIMI score was used to predict mortality using DL and feature selection methods from ML algorithms. The main performance metric was the area under the receiver operating characteristic curve (AUC). The DL and ML model constructed using ML feature selection outperforms the conventional risk scoring score, TIMI (AUC 0.75). DL built from ML features (AUC ranging from 0.93 to 0.95) outscored DL built from all features (AUC 0.93). The TIMI score underestimates mortality in the elderly. TIMI predicts 18.4% higher mortality than the DL algorithm (44.7%). All ML feature selection algorithms identify age, fasting blood glucose, heart rate, Killip class, oral hypoglycemic agent, systolic blood pressure, and total cholesterol as common predictors of mortality in the elderly. In a multi-ethnic population, DL outperformed the TIMI risk score in classifying elderly STEMI patients. ML improves death prediction by identifying separate characteristics in older Asian populations. Continuous testing and validation will improve future risk classification, management, and results.

Background Conventional risk score for predicting in-hospital mortality following Acute Coronary Syndrome (ACS) is not catered for Asian patients and requires different types of scoring algorithms for STEMI and NSTEMI patients. Objective To derive a single algorithm using deep learning and machine learning for the prediction and identification of factors associated with in-hospital mortality in Asian patients with ACS and to compare performance to a conventional risk score. Methods The Malaysian National Cardiovascular Disease Database (NCVD) registry, is a multi-ethnic, heterogeneous database spanning from 2006–2017. It was used for in-hospital mortality model development with 54 variables considered for patients with STEMI and Non-STEMI (NSTEMI). Mortality prediction was analyzed using feature selection methods with machine learning algorithms. Deep learning algorithm using features selected from machine learning was compared to Thrombolysis in Myocardial Infarction (TIMI) score. Results A total of 68528 patients were included in the analysis. Deep learning models constructed using all features and selected features from machine learning resulted in higher performance than machine learning and TIMI risk score (p < 0.0001 for all). The best model in this study is the combination of features selected from the SVM algorithm with a deep learning classifier. The DL (SVM selected var) algorithm demonstrated the highest predictive performance with the least number of predictors (14 predictors) for in-hospital prediction of STEMI patients (AUC = 0.96, 95% CI: 0.95–0.96). In NSTEMI in-hospital prediction, DL (RF selected var) (AUC = 0.96, 95% CI: 0.95–0.96, reported slightly higher AUC compared to DL (SVM selected var) (AUC = 0.95, 95% CI: 0.94–0.95). There was no significant difference between DL (SVM selected var) algorithm and DL (RF selected var) algorithm (p = 0.5). When compared to the DL (SVM selected var) model, the TIMI score underestimates patients’ risk of mortality. TIMI risk score correctly identified 13.08% of the high-risk patient’s non-survival vs 24.7% for the DL model and 4.65% vs 19.7% of the high-risk patient’s non-survival for NSTEMI. Age, heart rate, Killip class, cardiac catheterization, oral hypoglycemia use and antiarrhythmic agent were found to be common predictors of in-hospital mortality across all ML feature selection models in this study. The final algorithm was converted into an online tool with a database for continuous data archiving for prospective validation. Conclusions ACS patients were better classified using a combination of machine learning and deep learning in a multi-ethnic Asian population when compared to TIMI scoring. Machine learning enables the identification of distinct factors in individual Asian populations to improve mortality prediction. Continuous testing and validation will allow for better risk stratification in the future, potentially altering management and outcomes.

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Predictions of mortality in Asian women following STEMI have been the subject of limited studies. This study aims to develop and validate prediction models for in-hospital mortality following STEMI in Asian women using machine learning (ML) and stacked ensemble learning (EL) techniques, and to compare the performance of the algorithms to that of a conventional risk scoring method. From 2006 to 2016, data on multi-ethnic Asian women admitted with STEMI from the Malaysian National Cardiovascular Disease Database (NCVD-ACS) registry were collected. Developed algorithms were compared to the Thrombolysis in Myocardial Infarction Risk score (TIMI) and a ML model constructed using data from the general STEMI population. Predictors for ML models were selected using iterative feature selection comprises of feature importance and sequential backward elimination. The machine learning models developed using ML feature selection (AUC ranging from 0.60–0.93) outperforms the conventional risk score, TIMI (AUC 0.81). Individual ML model, SVM Linear with selected features performed better than the best performed stacked EL model (AUC:0.934, CI: 0.893–0.975 vs AUC: 0.914, CI: 0.871–0.957). The women specific model also performs better than the general non-gender specific model (AUC: 0.919, CI: 0.874–0.965). Systolic blood pressure, Killip class, fasting blood glucose, beta-blocker, ACE inhibitor, and oral hypoglycemic agent are identified as common predictors of mortality for women. In multi-ethnic populations, Asian women with STEMI were more accurately classified by ML and stacked EL than by the TIMI risk score. It has also been determined that women-specific ML models perform better than the standard STEMI model. In the future, ongoing testing and validation can improve the clinical care provided to women with STEMI.