ACS mortality prediction in Asian in-hospital patients with deep learning using machine learning feature selection

Abstract Background Thrombolysis in Myocardial infarction (TIMI) is used in predicting the mortality rate of the acute coronary syndrome (ACS) patients. TIMI was developed based on the Western cohort with limited data on the Asian cohort. There are separate TIMI scores for STEMI and NSTEMI. Deep learning (DL) and machine learning (ML) algorithms such as support vector machine (SVM) in population-specific dataset resulted in a higher area under the curve (AUC) to TIMI. The limitation of DL is selected features by the algorithm is unknown compared to ML algorithms. Purpose To construct a single in-hospital mortality risk scoring system that combines SVM feature importance and the DL algorithm in ASIAN patients with ACS that is applicable for both STEMI and NSTEMI patients. To investigate DL performance constructed using predictors selected from SVM feature extraction and DL using complete features and compare with TIMI risk score for STEMI and NSTEMI patients. Methods We constructed four algorithms: i) DL and SVM algorithm with feature selected from SVM variable importance, ii) DL and SVM algorithm without feature selection. SVM feature importance with the backward elimination method is used to select and rank important variables. We used registry data from the National Cardiovascular Disease Database of 13190 patient's data. Fifty-four parameters including demographics, cardiovascular risk, medications and clinical variables were considered. AUC was used as the performance evaluation metric. All algorithms were validated using validation dataset and compared to the conventional TIMI for STEMI and NSTEMI. Results Validation results in Figure 1 are by STEMI and NTEMI patients. Both DL algorithms outperformed ML and TIMI score on validation data. Similar performance is observed for DL and SVM algorithms using all predictors (54 predictors) with DL and SVM algorithm using selected predictors (14 predictors). Predictors selected by the SVM feature selection are: age, heart rate, Killip class, fasting blood glucose, ST-elevation, CABG, cardiac catheterization, angina episode, HDLC, LDC, other lipid-lowering agents, statin, anti-arrhythmic agent, oralhypogly. CABG and pharmacotherapy drugs as selected predictors improve mortality prediction compared to TIMI score. In DL, 25.87% of STEMI patients and 19.71% of NSTEMI patients are estimated as high risk (risk probabilities of >50%). TIMI underestimated the risk of mortality of high-risk patients (≥5 risk scores) with 13.08% from STEMI patients and 4.65% from NSTEMI patients (Figure 2). Conclusions In the ASIAN multi-ethnicity population, patients with ACS can be better classified using one single algorithm compared to the conventional method like TIMI which requires two different scores. Combining ML feature selection with DL allows the identification of distinct factors related to in-hospital mortality of ACS patients in a unique ASIAN population for better mortality prediction. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Technology Development Fund 1 Figure 1. Performance resultsFigure 2. Analysis on the validation set