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Evaluation of optimization techniques for variable selection in logistic regression applied to diagnosis of myocardial infarction

By Adam Kiezun, I-Ting Angelina Lee and Noam Shomron

Abstract

Logistic regression is often used to help make medical decisions with binary outcomes. Here we evaluate the use of several methods for selection of variables in logistic regression. We use a large dataset to predict the diagnosis of myocardial infarction in patients reporting to an emergency room with chest pain. Our results indicate that some of the examined methods are well suited for variable selection in logistic regression and that our model, and our myocardial infarction risk calculator, can be an additional tool to aid physicians in myocardial infarction diagnosis

Topics: Hypothesis
Publisher: Biomedical Informatics Publishing Group
OAI identifier: oai:pubmedcentral.nih.gov:2655051
Provided by: PubMed Central
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