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Diagnosing Angina Using a Simple Neural Network Architecture

Bulgiba, A.M., (2006) Diagnosing Angina Using a Simple Neural Network Architecture. Journal of the University of Malaya Medical Centre (JUMMEC), 9 (1). pp. 39-43. ISSN 1823-7339

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Official URL: http://jummec.um.edu.my

Affiliations

University of Malaya. Faculty of Medicine. Dept. of Social and Preventive Medicine

Abstract

ABSTRACT: 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.

Item Type:Journal
Keywords:angina, diagnosis, prediction, decision-support, neural networks, perceptrons
Subjects:R Medicine, Dentistry, Pharmacy, Nursing
ID Code:6365

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