Finding improved predictive models with Generalized Boosted Models on Hungarian Myocardial Infarction Registry
Peter Piros, Rita Fleiner, Levente Kovács · 2020
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'.