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

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Title		Evaluation of optimization techniques for variable selection in logistic regression applied to diagnosis of myocardial infarction
Authors		Adam Kiezun¹, I-Ting Angelina Lee¹ and Noam Shomron^2*
Affiliation		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
Email		nshomron@post.tau.ac.il; * Corresponding authors
Phone		+972-3-640-6594
Fax		+972-3-640-7432
Article Type		Hypothesis
Date		received January 04, 2009; accepted January 27, 2009; published February 28, 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
Citation		Kiezun et al., Bioinformation 3(7): 311-313 (2009)
Edited by		P. Kangueane
ISSN		0973-2063
Publisher		Biomedical Informatics
License		This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.