An IoT-Based Heart Disease Detection System Using RNN

With the growing social pressure, most people are facing health problems and most of these are causing because of frequent heart attacks. It is very important to design an effective system that can diagnose the forthcoming happening of heart attacks or atrial fibrillations. As IoT (Internet of Things) is the most emerging and prominent technology in the modern era, a combination of the personal computer and IoT has become very efficient technology. The combined system provides various features to the user such as in emergencies to detect heart disease possibility through symptoms, sending messages to doctors according to the stage of the possibility of disease, and helps in fixing it. In case of an emergency, the system sends an emergency report to the desired doctor. In this paper, we propose an IoT based heart disease detection system using a Recurrent Neural Network (RNN). In our approach, we use Long Short Term Memory (LSTM) algorithm. The proposed system can perform the diagnosis of heart disease using RNN. The developed system helps the physician to prescribe patients without being present physically. The estimation of the result claims that the proposed system can detect heart disease efficiently.