A Modern Approach to AI Assistant for Heart Disease Detection by Heart Sound through created e-Stethoscope

In this work, first, we created an electronic stethoscope (e-Stethoscope) of very low cost that converts the acoustic sound waves obtained through the chest piece into electrical signals and can amplify heart murmurs and noises created by the heart valves. This paper presents an effective way of predicting heart diseases based on heart sounds produced by this e-stethoscope. Our prediction system collects heart sounds from patients using this e-stethoscope and then analyzes them to predict the disease by running various Machine-learning and Deep-learning models like KNN, SVM, Decision Tree, Random Forest, MLP Classifier, ANN, 1D CNN, 2D CNN, etc. We analyzed the results through the 3 datasets, Physionet, Pascal, and Our Collected Heart Dataset. MLP classifier and ANN both performed well on our dataset. A modern heart sound database platform is developed to impact the telemedicine sector worldwide. This telemedicine service may help to cut costs and travel time massively.