Medical Diagnosis of Ailments Through Supervised Learning Techniques on Sounds of the Heart and Lungs

Abstract

Auscultation is a medical technique to decipher ailments of human body through careful observation of heart and lung sounds. This project is a sincere effort to develop a low-cost stethoscope solution which can record and store the internal body sounds of patients’ heart and lungs which will assist physicians in taking careful observations and tagging illnesses to the sounds, and training a machine learning model on recorded and tagged sounds to autosuggest the illness. There exist much matured medical diagnostic solutions in the country, but all of these solutions require a specific amount of time as well as incur a great cost to the patient. Machine learning in computation has evolved as a key driving force in the industry nowadays serving in a range of applications. Hence, we have extensively used various classifiers to train and test the audio data of patients and generate a viable diagnostic output which will serve as a primary guide to a medical practitioner. We hope this project serves its cause and has an impact on society through proper channelization of resources.