Classification of normal and abnormal heart by classifying PCG signal using MFCC coefficients and CGP-ANN classifier

Globally, A leading cause of death is heart disease and a serious public health concern. The anomalies in sound appears before the symptoms. sounds are type auscultation, which process dealing with body that generates due to mechanical vibrations organs, Auscultation potential method medical science detect abnormalities case suspicion patient follows up referral for other evaluations, such as an electrocardiogram. In sciences early detection symptoms major importance, this research work good step toward symptom appearing by processing phonocardiogram (PCG) signal. paper PCG signals classified utilizing features Mel frequency cepstral coefficients (MFCC) through Cartesian Genetic Programming - Artificial Network (CGP-ANN) Classifier. diagnostic accuracy proposed methodology found 99.50% more than classifiers like Support Vector Machine (SVM) Convolutional Neural (CNN). model compared models can prove performance superiority system.