Further Evolution of Mortality Prediction with Ensemble-based Models on Hungarian Myocardial Infarction Registry

In the current study, we present a new approach to predict 30-day and 1-year mortality of patients hospitalized with acute myocardial infarction. The dataset of this research is originated from Hungarian Myocardial Infarction Registry, a full, real-world, unfiltered database of myocardial infarctions from year 2014 to 2016 (n = 47,391). The new approach is based on ensembling and uses the prediction capability of different (already ensembled, in some cases) models like Random Forest, General Boosting Machine, Neural Network and Generalized Linear Model. We previously presented more different modelling techniques with the same target on the same dataset, and this new ensemble-based way of prediction proved to be the best among all the others. By numbers, this means 0.856 ROC AUC (area under the receiver operating characteristic curve) for the 30-day, and 0.839 ROC AUC for the 1-year mortality, both measured on validation datasets. We came to the conclusion that the combination of machine learning algorithms and regression models results the best performance in mortality prediction on the dataset of HUMIR.