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Using neural networks and just nine patient-reportable factors of screen for AMI

The study investigated the effect of different input selections on the performance of artificial neural networks in screening for acute myocardial infarction (AMI) in Malaysian patients complaining of chest pain. We used hospital data to create neural networks with four input selections and used the...

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Main Authors: Bulgiba, A.M., Fisher, M.H.
Format: Article
Published: 2006
Subjects:
R Medicine
Online Access:http://eprints.um.edu.my/3083/
http://www.ncbi.nlm.nih.gov/pubmed/17023409
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spelling my.um.eprints.30832012-05-03T02:56:24Z http://eprints.um.edu.my/3083/ Using neural networks and just nine patient-reportable factors of screen for AMI Bulgiba, A.M. Fisher, M.H. R Medicine The study investigated the effect of different input selections on the performance of artificial neural networks in screening for acute myocardial infarction (AMI) in Malaysian patients complaining of chest pain. We used hospital data to create neural networks with four input selections and used these to diagnose AMI. A 10-fold cross-validation and committee approach was used. All the neural networks using various input selections outperformed a multiple logistic regression model, although the difference was not statistically significant. The neural networks achieved an area under the ROC curve of 0.792 using nine inputs, whereas multiple logistic regression achieved 0.739 using 64 inputs. Sensitivity levels of over 90 per cent were achieved using low output threshold levels. Specificity levels of over 90 per cent were achieved using threshold levels of 0.4-0.5. Thus neural networks can perform as well as multiple logistic regression models even when using far fewer inputs. 2006 Article PeerReviewed Bulgiba, A.M. and Fisher, M.H. (2006) Using neural networks and just nine patient-reportable factors of screen for AMI. Health Informatics Journal, 12 (3). pp. 213-225. ISSN 1460-4582 http://www.ncbi.nlm.nih.gov/pubmed/17023409 10.1177/1460458206066665
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic R Medicine
spellingShingle R Medicine
Bulgiba, A.M.
Fisher, M.H.
Using neural networks and just nine patient-reportable factors of screen for AMI
description The study investigated the effect of different input selections on the performance of artificial neural networks in screening for acute myocardial infarction (AMI) in Malaysian patients complaining of chest pain. We used hospital data to create neural networks with four input selections and used these to diagnose AMI. A 10-fold cross-validation and committee approach was used. All the neural networks using various input selections outperformed a multiple logistic regression model, although the difference was not statistically significant. The neural networks achieved an area under the ROC curve of 0.792 using nine inputs, whereas multiple logistic regression achieved 0.739 using 64 inputs. Sensitivity levels of over 90 per cent were achieved using low output threshold levels. Specificity levels of over 90 per cent were achieved using threshold levels of 0.4-0.5. Thus neural networks can perform as well as multiple logistic regression models even when using far fewer inputs.
format Article
author Bulgiba, A.M.
Fisher, M.H.
author_facet Bulgiba, A.M.
Fisher, M.H.
author_sort Bulgiba, A.M.
title Using neural networks and just nine patient-reportable factors of screen for AMI
title_short Using neural networks and just nine patient-reportable factors of screen for AMI
title_full Using neural networks and just nine patient-reportable factors of screen for AMI
title_fullStr Using neural networks and just nine patient-reportable factors of screen for AMI
title_full_unstemmed Using neural networks and just nine patient-reportable factors of screen for AMI
title_sort using neural networks and just nine patient-reportable factors of screen for ami
publishDate 2006
url http://eprints.um.edu.my/3083/
http://www.ncbi.nlm.nih.gov/pubmed/17023409
_version_ 1643687042260926464
score 13.211869

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