Discriminative Neural Network for Coronary Heart Disease Detection

Abstract

Cardiovascular disease is one of the commonest diseases and main causes of death over the world. As the major type of cardiovascular diseases, correct and timely diagnosis of coronary heart disease (CHD) is very essential. Traditional back-propagation (BP) neural network aims to train a multilayer feedforward neural network which transforms data into the feature space to learn good decision boundaries. However, the performance of BP neural network tends to deteriorate when dealing with complexity medical diagnostic tasks. To improve the detection of CHD, this study proposes a discriminative neural network called DNN. DNN explores discriminative information by maximizing the difference of the compactness within each class and separability between different classes. DNN integrates the discriminative information into the framework of BP neural network, and can be easily implemented by the existing neural network software. Experimental results on Z-Alizadeh Sani dataset show that DNN achieves satisfactory performance in sensitivity, specificity, accuracy and receiver operating characteristic (ROC) curve.