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

2020 IEEE 15th International Conference of System of Systems Engineering (SoSE), 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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