Finding improved predictive models with Generalized Boosted Models on Hungarian Myocardial Infarction Registry

In this paper, we present new predictive modelling results achieved with Generalized Boosted Models (GBM) on the dataset of Hungarian Myocardial Infarction Registry (n = 47,391). We investigated patients hospitalized with acute myocardial infarction from two aspects, namely the 30-day and 1-year mortality. The ROC AUC values of our new models for predicting 30-day mortality were 0.847 and 0.839 (training and validation set), while for the 1-year models these were 0.828 and 0.821, respectively. These performance values represent a strong and stable learner with almost the similar predictive power as our previously published random forest models'.