Disease prediction system using wearable sensor data using ANN and Fuzzy Classification

Abstract

Health monitoring through the wearable sensor is very common in today's life and it becomes a kind of trend. Numerous wearable sensors resemble keen watches, Activity trackers and a lot more give the wellbeing parameters like heartbeat rate, Blood pressure, calorie consumed and a lot more regularly or on fixed availability. Just by monitoring the health parameters users or patients cannot predict their health status for the future nor they predict any ill health conditions that eventually lead to some diseases. Numerous procedures are existed to foresee a specific illness dependent on the past dataset utilizing the profound learning strategy. The greater part of these methods utilizing the immense measure of information for profound figuring out how to anticipate a couple of sorts of sicknesses. There is a need of great importance to anticipate the infection inclination utilizing the wearable sensor's moderate measure of information. With the goal that the clients of these gadgets can have increased opportunities to improve their wellbeing conditions as opposed to simply checking. So as a modest advance towards this proposed model uses the K implies grouping and Artificial neural system to anticipate the illness inclination status and this procedure is fueled with the Fuzzy Classification model to arrange the information into various maladies.