Diagnosing angina using a simple neural network architecture

The aim of the study was to research the use of a simple neural network in diagnosing angina in patients complaining of chest pain. A total of 887 records were extracted from the electronic medical record system (EMR) in Selayang Hospital, Malaysia. Simple neural networks (simple perceptrons) were b...

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Main Author: Bulgiba, Awang
Format: Article
Language:English
Published: University of Malaya Medical Centre 2006
Subjects:
Online Access:http://eprints.um.edu.my/3084/1/Diagnosing_angina_using_a_simple_neural_network_architecture.pdf
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author Bulgiba, Awang
author_facet Bulgiba, Awang
author_sort Bulgiba, Awang
collection UM
description The aim of the study was to research the use of a simple neural network in diagnosing angina in patients complaining of chest pain. A total of 887 records were extracted from the electronic medical record system (EMR) in Selayang Hospital, Malaysia. Simple neural networks (simple perceptrons) were built and trained using a subset of 470 records with and without pre-processing using principal components analysis (PCA). These were subsequently tested on another subset of 417 records. Average sensitivity of 80.75 (95 CI 79.54, 81.96), specificity of 41.64 (95 CI 40.13, 43.15), PPV of 46.73 (95 CI 45.20, 48.26) and NPV of 77.39 (95 CI 76.11, 78.67) were achieved with the simple perceptron. When PCA pre-processing was used, the perceptrons had a sensitivity of 1.43 (95 CI 1.06, 1.80), specificity of 98.32 (95 CI 97.92, 98.72), PPV of 32.95 (95 CI 31.51, 34.39) and NPV of 61.33 (95 CI 59.84, 62.82). These results show that it is possible for a simple neural network to have respectable sensitivity and specificity levels for angina. (JUMMEC 2006; 9(1): 39-43)
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spelling um.eprints-30842019-02-25T08:01:38Z http://eprints.um.edu.my/3084/ Diagnosing angina using a simple neural network architecture Bulgiba, Awang R Medicine The aim of the study was to research the use of a simple neural network in diagnosing angina in patients complaining of chest pain. A total of 887 records were extracted from the electronic medical record system (EMR) in Selayang Hospital, Malaysia. Simple neural networks (simple perceptrons) were built and trained using a subset of 470 records with and without pre-processing using principal components analysis (PCA). These were subsequently tested on another subset of 417 records. Average sensitivity of 80.75 (95 CI 79.54, 81.96), specificity of 41.64 (95 CI 40.13, 43.15), PPV of 46.73 (95 CI 45.20, 48.26) and NPV of 77.39 (95 CI 76.11, 78.67) were achieved with the simple perceptron. When PCA pre-processing was used, the perceptrons had a sensitivity of 1.43 (95 CI 1.06, 1.80), specificity of 98.32 (95 CI 97.92, 98.72), PPV of 32.95 (95 CI 31.51, 34.39) and NPV of 61.33 (95 CI 59.84, 62.82). These results show that it is possible for a simple neural network to have respectable sensitivity and specificity levels for angina. (JUMMEC 2006; 9(1): 39-43) University of Malaya Medical Centre 2006 Article PeerReviewed application/pdf en http://eprints.um.edu.my/3084/1/Diagnosing_angina_using_a_simple_neural_network_architecture.pdf Bulgiba, Awang (2006) Diagnosing angina using a simple neural network architecture. Journal of Health and Translational Medicine, 9 (1). pp. 39-43. ISSN 1823-7339, http://jummec.um.edu.my/pastissues/JUMMEC%20Volume%209%281%29%202006.pdf#page=44
spellingShingle R Medicine
Bulgiba, Awang
Diagnosing angina using a simple neural network architecture
title Diagnosing angina using a simple neural network architecture
title_full Diagnosing angina using a simple neural network architecture
title_fullStr Diagnosing angina using a simple neural network architecture
title_full_unstemmed Diagnosing angina using a simple neural network architecture
title_short Diagnosing angina using a simple neural network architecture
title_sort diagnosing angina using a simple neural network architecture
topic R Medicine
url http://eprints.um.edu.my/3084/1/Diagnosing_angina_using_a_simple_neural_network_architecture.pdf
work_keys_str_mv AT bulgibaawang diagnosinganginausingasimpleneuralnetworkarchitecture