| Evaluating variable selection methods for diagnosis of myocardial infarction. | |
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MedLine Citation:
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PMID: 10566358 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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This paper evaluates the variable selection performed by several machine-learning techniques on a myocardial infarction data set. The focus of this work is to determine which of 43 input variables are considered relevant for prediction of myocardial infarction. The algorithms investigated were logistic regression (with stepwise, forward, and backward selection), backpropagation for multilayer perceptrons (input relevance determination), Bayesian neural networks (automatic relevance determination), and rough sets. An independent method (self-organizing maps) was then used to evaluate and visualize the different subsets of predictor variables. Results show good agreement on some predictors, but also variability among different methods; only one variable was selected by all models. |
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Authors:
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S Dreiseitl; L Ohno-Machado; S Vinterbo |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, P.H.S. |
Journal Detail:
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Title: Proceedings / AMIA ... Annual Symposium. AMIA Symposium Volume: - ISSN: 1531-605X ISO Abbreviation: Proc AMIA Symp Publication Date: 1999 |
Date Detail:
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Created Date: 2000-02-01 Completed Date: 2000-02-01 Revised Date: 2012-10-09 |
Medline Journal Info:
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Nlm Unique ID: 100883449 Medline TA: Proc AMIA Symp Country: UNITED STATES |
Other Details:
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Languages: eng Pagination: 246-50 Citation Subset: IM |
Affiliation:
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Harvard Medical School/Massachusetts Institute of Technology, Division of Health Sciences and Technology, Boston, USA. sdreisei@dsg.harvard.edu |
Export Citation:
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APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* Artificial Intelligence* Chest Pain / etiology Diagnosis, Computer-Assisted Evaluation Studies as Topic Humans Logistic Models Mathematics Myocardial Infarction / diagnosis* Neural Networks (Computer) |
| Grant Support | |
ID/Acronym/Agency:
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467-MZ-802289//PHS HHS; R01 LM006538/LM/NLM NIH HHS; R29 LM06538-01/LM/NLM NIH HHS |
| Comments/Corrections | |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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