# Heart Diseases Diagnose via Mobile Application
###### Abstract  
One of the oldest and most common methods of diagnosing heart abnormalities is auscultation. Even for experienced medical doctors, it is not an easy task to detect abnormal patterns in the heart sounds. Most digital stethoscopes are now capable of recording and transferring heart sounds. Moreover, it is proven that auscultation records can be classified as healthy or unhealthy via artificial intelligence techniques. In this work, an artificial intelligence-powered mobile application that works in a connectionless fashion is presented. According to the clinical experiments, the mobile application can detect heart abnormalities with approximately 92% accuracy, which is comparable to if not better than humans since only a small number of well-trained cardiologists can analyze auscultation records better than artificial intelligence. Using the diagnostic ability of artificial intelligence in a mobile application would change the classical way of auscultation for heart disease diagnosis.  
heart diseases; auscultation; machine learning; telemedicine; digital stethoscope +
Footnote †: journal: Applied  
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telemedicine purposes. Currently, few telemedicine products are available for analyzing heart sound recordings [11; 12; 13; 14]. These telemedicine solutions take the heart sounds via a digital stethoscope and upload the recording to the cloud for classification.  
In this work, a mobile application designed to operate on mobile devices for telemedicine purposes is presented. The classification module of the software was trained and validated with the PhysioNet/CinC Challenge 2016 database. Since the PhysioNet dataset consists of abnormal recordings from coronary artery diseases, aortic stenosis, mitral regurgitation, mitral valve prolapse, and pathological murmurs conditions, the classification algorithm is expected to detect those abnormalities. The real-life success of the algorithm has been tested at the clinic using the mobile application. The algorithm can detect abnormal heart sounds in the validation set with 93% accuracy, and the mobile software that is powered by the same algorithm can detect abnormal samples with nearly the same sensitivity rate. The algorithm uses an ensemble nearest neighbors classifier, and features extracted from both Mel-frequency cepstral coefficients (MFFCs) and statistical properties of the fundamental heart sounds. Unlike available commercial products [11; 12; 13], the software works in an embedded fashion and does not need an Internet connection to upload the heart sound to the cloud for classification. Every five seconds, it analyzes the heart sound and prompts the predicted diagnosis to the screen.  
The software presented in this work is intended to be used both in clinic settings by medical doctors and by ordinary people at home. Therefore, the software needs to be verified in real-life circumstances. For this, the software was installed on an Android device and the mobile application was tested at Ankara City Hospital, Cardiology Department by expert medical doctors. It is approved by the medical doctors that the artificial intelligence-powered mobile application can distinguish healthy and unhealthy individuals with 93% accuracy.