A Deep Learning Model for Prediction of Cardiovascular Disease Using Heart Sound

Cardiovascular disease is the most emerging disease in this generation of youth. You need to know about your heart condition to overcome this disease appropriately. An electronic stethoscope is used in the cardiac auscultation technique to listen to and analyze heart sounds. Several pathologic cardiac diseases can be detected by auscultation of the heart sounds. Unlike heart murmurs, the sounds of the heart are separate; brief auditory phenomena usually originate from a single source. This article proposes a deep-learning model for predicting cardiovascular disease. The combined deep learning model uses the MFCC and LSTM for feature extraction and prediction of cardiovascular disease. The model achieved an accuracy of 94.3%. The sound dataset used in this work is retrieved from the UC Irvine Machine Learning Repository. The main focus of this research is to create an automated system that can assist doctors in identifying normal and abnormal heart sounds.