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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52506

Title: Comparing machine learning and regression models for mortality prediction based on the Hungarian Myocardial Infarction Registry
Authors: Peter Piros
Tamás Ferenci
Rita Fleiner
Péter Andréka
Hamido Fujita
László Fozo
Levente Kovács
András Jánosi
Contributors: 國立臺灣海洋大學:資訊工程學系
Keywords: Myocardial Infarction Registry
Myocardial Infarction
Hungarian Myocardial Infarction Registry
Mortality prediction
Decision tree
Neural network
Date: 2019-05
Issue Date: 2019-11-18T08:07:48Z
Publisher: Knowledge-Based Systems
Abstract: Abstract: The objective of the current study is to compare the relative performance of decision tree, neural network, and logistic regression for predicting 30-day and 1-year mortality in a real-word, unfiltered dataset () of patients hospitalized with acute myocardial infarction. Area under the ROC curve (AUC) was used for evaluating performance of a learning algorithm. For 30-day mortality, we achieved an average of 0.788 for decision tree models, 0.837 for neural net models and 0.836 for regression models on training set (on validation sets: 0.774, 0.835 and 0.834, respectively). For 1-year mortality, the averages were 0.754 for decision tree models, 0.8194 for neural net models and 0.8191 for regression models (on validation sets: 0.743, 0.8179 and 0.8176, respectively). Differences were non-significant between neural network and regression, but both significantly outperformed decision trees. The machine learning methods investigated in the present study could not outperform traditional regression modelling for mortality prediction in myocardial infarction.
Relation: 179 pp.1-7
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52506
Appears in Collections:[資訊工程學系] 期刊論文

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