# Heart Diseases Diagnose via Mobile Application
## 4 Discussion  
Medical doctors without special auscultation training cannot perform well in detecting murmurs. The accuracy of pediatricians in detecting heart murmurs is 24% [26]. Another study states that general practitioners can diagnose significant valvular heart diseases with a sensitivity of 43% and specificity of 69% but cardiologists can predict up to a sensitivity of 81% [27]. Even though some trained cardiologists can perform well, clinicians have variable auscultation performance [28; 29]. Therefore, both cardiologists and non-cardiologist need a preliminary diagnostic tool to boost their auscultation accuracy. Similarly, non-trained clinicians have a low accuracy rate with electrocardiogram data and only cardiologists can diagnose successfully [30; 31]. Moreover, electrocardiogram-based artificial intelligence models such as KardiaMobile and Eko AI software can perform as well as cardiologists [32]. Eko AI software requires users to upload their data to the cloud for computation. Unlike these commercial products, the developed application can run solely on the mobile device's hardware thanks to its informative features and robust classification algorithm. Regardless of having an internet connection, one can evaluate his or her heart health condition instantly with a sound recorder, preferably with a regular digital stethoscope. As a result, users do not need to upload the recordings to a server or a cloud for evaluation. This feature provides data privacy and flexibility for users. In the near future, with the proliferation of AI-powered applications for telemedicine purposes, data privacy issues will be more important.  
Another key feature of the application is that it accepts the data from the start and it perpetually outputs the predicted diagnosis result in five-second periods. For future works, this property can be adopted to intensive-care units to trigger an alarm when an abnormality is detected. Cardiac event monitors control electrocardiogram signals to decide whether the patient's heart continues its function or not. Those patient monitoring systems do not provide any information about instant heart abnormalities. If this process can be converted to listening to the heart and continuously analyzing its condition, the effectiveness of patient monitoring systems will increase.  
Given that we have a global pandemic, the medical system is under pressure, and clinicians are overwhelmed by the increasing number of patients caused by coronavirus disease 2019 (COVID-19). Telemedicine tools like the one presented in this study can reduce the number of healthy individuals visiting hospitals and as a result, take the burden off the medical system. Since the mobile application is designed to be used not only for experts but also by users of no medical education, verification data should consist of sounds that are recorded by ordinary users. To mimic the behavior of users with no medical background, during the verification tests, medical staff pointed the digital stethoscope at random locations on the chest near the heart. The verification results were successful but this is partly due to the digital stethoscope's filter property at the device level that can eliminate unwanted sounds produced from other organs such as lungs, etc. To compensate for the noise effect, an additional pre-processing block could be added in future works.