HeartFit: An Accurate Platform for Heart Murmur Diagnosis Utilizing Deep Learning
Ankit Gupta, George Tang, Sylesh Suresh

TL;DR
HeartFit is a deep learning-based platform that enables users to diagnose heart murmurs accurately using a mobile app and inexpensive hardware, offering a cost-effective alternative to traditional clinical methods.
Contribution
The paper introduces a novel, accessible platform combining a custom stethoscope and deep learning for self-administered heart murmur diagnosis, outperforming clinical accuracy.
Findings
Achieved 95.5% accuracy in classifying heart murmurs
Validated on unseen data with a high F-beta score of 0.9545
Demonstrated potential to replace costly clinical procedures
Abstract
Cardiovascular disease (CD) is the number one leading cause of death worldwide, accounting for more than 17 million deaths in 2015. Critical indicators of CD include heart murmurs, intense sounds emitted by the heart during periods of irregular blood flow. Current diagnosis of heart murmurs relies on echocardiography (ECHO), which costs thousands of dollars and medical professionals to analyze the results, making it very unsuitable for areas with inadequate medical facilities. Thus, there is a need for an accessible alternative. Based on a simple interface and deep learning, HeartFit allows users to administer diagnoses themselves. An inexpensive, custom designed stethoscope in conjunction with a mobile application allows users to record and upload audio of their heart to a database. Using a deep learning network architecture, the database classifies the audio and returns the diagnosis…
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Taxonomy
TopicsECG Monitoring and Analysis
