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Evaluation of optimization techniques for variable selection in logistic regression applied to diagnosis of myocardial infarction.
MedLine Citation:
PMID:  19293999     Owner:  NLM     Status:  PubMed-not-MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Adam Kiezun; I-Ting Angelina Lee; Noam Shomron
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Publication Detail:
Type:  Journal Article     Date:  2009-02-28
Journal Detail:
Title:  Bioinformation     Volume:  3     ISSN:  0973-2063     ISO Abbreviation:  Bioinformation     Publication Date:  2009  
Date Detail:
Created Date:  2009-03-18     Completed Date:  2010-06-09     Revised Date:  2013-05-23    
Medline Journal Info:
Nlm Unique ID:  101258255     Medline TA:  Bioinformation     Country:  Singapore    
Other Details:
Languages:  eng     Pagination:  311-3     Citation Subset:  -    
Affiliation:
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
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