Smart Health Prediction System

As the human life march towards the modern amenities all the sectors of life become more and more advanced, Health care is not spared from this. The revolutionary health care predictions actually brings more relief for elderly or bed ridden patients. Internet of things actually plays vital role into this paradigm as it helps to collect and process the data from the wearable devices of patients. This data often transferred to the server though the wireless communication. Many existing systems are there where these data is been monitored manually or by loosely coupled systems which yields less accurate results in prediction. To get the impression of sensors proposed model deploys a simulation environment so that data like sensors can be generated in the regular environment based on some facts like  Number of hours of sleep  Number of meals taken  Number of Calories burnt  Blood pressure  Pulse rate  Blood Sugar Level and etc... As the data reaches to the server through the wireless network all the data is been collected into a list and then they are preprocessed for the required attributes. Once the data is preprocessed then the attributes are subjected to the K -Means clustering to cluster the relevant data. And the distribution of data is been carried out using Information Gain Estimation. And the whole process is deployed using ANN. And this process is terminates using Random Forest Classification process to predict the health prone predictions.