Utilization of 4 types of Artificial Neural Network on the diagnosis of valve-physiological heart disease from heart sounds

The classification of the sound heart into different valve-physiological heart disease categories is a complex pattern recognition task. This paper will purpose sound heart recognition for diagnosing heart disease with 4 type of Artificial Neural Network (ANN). We develop a simple model for the recognition of heart sounds, and demonstrate its utility in identifying features useful in diagnosis. We then present a prototype system intended to aid in heart sound analysis. Based on a wavelet decomposition of the sounds, feature vectors are formed and ANNs finds use in classification of Heart valve diseases for its discriminative training ability and easy implementation. The heart sound diseases classes considered for the purpose of this study were classified into normal heart sound and the other six valve physiological heart categories. 4 type of ANN which used for this approach are Multilayer perceptron!(MLP), Back Propagation Algorithm (BPA), Elman Neural Network (ENN) and Radial Basis Function (RBF) Network. Using these ANN classifiers would appeared ability of classifying heart disease and will be shown an accuracy of 81.25 % for MLP, 87.17% for BPA, 91.59% for ENN and 96.42% for RBF was achieved.