Diagnosing angina using a simple neural network architecture - Bulgiba, Awang (University of Malaya Medical Centre, 2006)
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)