Random Forest-based predictive modelling on Hungarian Myocardial Infarction Registry (original) (raw)

2020

Abstract

The objective of the current study is to compare how our two tree-based machine learning algorithms can predict 30-day and 1-year mortality of patients hospitalized with acute myocardial infarction. The two algorithms were decision tree and random forest, and the source of dataset is Hungarian Myocardial Infarction Registry (n=47,391). As a result, we found that the ROC AUC values of Random Forest models for predicting 30-day mortality were 0.843 and 0.847 (training and validation set), while for the 1-year models these were 0.835 and 0.836, respectively. These numbers mean that, the Random Forest models were at least 5-6% better than the decision tree models, but in some cases the improvement is above 9%.

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