  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 to
the user. The model consists of a deep recurrent convolutional neural network
trained on 300 prelabeled heartbeat audio samples. After the model was
validated on a previously unseen set of 100 heartbeat audio samples, it
achieved a f beta score of 0.9545 and an accuracy of 95.5 percent. This value
exceeds that of clinical examination accuracy, which is around 83 percent to 91
percent and costs orders of magnitude less than ECHO, demonstrating the
effectiveness of the HeartFit platform. Through the platform, users can obtain
immediate, accurate diagnosis of heart murmurs without any professional medical
assistance, revolutionizing how we combat CD.
