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Bioinformation. 2009; 3(7): 311–313.
Published online Feb 28, 2009.
PMCID: PMC2655051
Evaluation of optimization techniques for variable selection in logistic regression applied to diagnosis of myocardial infarction
Adam Kiezun,1 I-Ting Angelina Lee,1 and Noam Shomron2*
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge,MA, 02139, USA
2Department of Cell and Developmental Biology, Sackler Faculty of Medicine, Tel Aviv University, 69978, Israel
*Noam Shomron: nshomron/at/post.tau.ac.il; Tel: +972-3-640-6594; Fax: +972-3-640-7432
Received January 4, 2009; Accepted January 27, 2009.
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.
Keywords: logistic regression, diagnostic markers, variable selection methods, myocardial infarction
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