Predicting 30-Day Mortality After An Acute Coronary Syndrome (Acs) Using Machine Learning Methods For Feature Selection, Classification And Visualisation

Hybrid combinations of feature selection, classification and visualisation using machine learning (ham) methods have the potential for enhanced understanding and 30-day mortality prediction of patients with cardiovascular disease using population-specific data. Identifying a feature selection method with a classifier algorithm that produces high performance in mortality studies is essential and has not been reported before. Feature selection methods such as Boruta, Random Forest (RF), Elastic Net (EN), Recursive Feature Elimination (RFE), learning vector quantization (LVQ), Genetic Algorithm (GA), Cluster Dendrogram (CD), Support Vector Machine (SI/M) and Logistic Regression (LR) were combined with RE SLM, LR, and EN classifiers for 30-day mortality prediction. ML models were constructed using 302 patients and 54 input variables from the Malaysian National Cardiovascular Disease Database. Validation of the best ML model was performed against Thrombolysis in Myocardial Infarction (TMI) using an additional dataset of 102 patients. The Self-Organising Feature Map (SOM) was used to visualise mortality-related factors post ACS. The performance of ML models using the area under the curve (AUC) ranged from 0.48 to 0.80. The best-performing model (AUC = 0.80) was a hybrid combination of the RF variable importance method, the sequential backward selection and the RF classifier using five predictors (age, triglyceride, creatinine, troponin, and total cholesterol). Comparison with T1M1 using an additional dataset resulted in the best ML model outperforming the TiM! score (AUC = 0.75 vs. AUC 0.60). The findings of this study will provide a basis for developing an online ML-based population-specific risk scoring calculator.