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Disease Pronenesss Prediciton through IoT and ANN


Vithal Waghmode , Modern Education Societys College of Engineering; Abhishek Srivastava, Modern Education Societys College of Engineering; Ankit Lokhande, Modern Education Societys College of Engineering; Tushar Shinde, Modern Education Societys College of Engineering; Niyamat Ujloomwale, Modern Education Societys College of Engineering


Fuzzy C means Clustering, Information Gain Theory Estimation, ANN


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 bedridden patients. Internet of things actually plays a vital role in this paradigm as it helps to collect and process the data from the wearable devices for patients. This data often transferred to the server though the wireless communication. Many existing systems are there where these data are being monitored manually or by loosely coupled systems which yields less accurate results in a 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 medical facts and also some parameters are captured by the real time pulse sensors. These collected data is synchronized to the prediction server in the said interval to predict the disease. To predict the disease proneness proposed model uses the Fuzzy C Means clustering and Artificial Neural network to yield the best possible prediction.

Other Details

Paper ID: IJSRDV7I40269
Published in: Volume : 7, Issue : 4
Publication Date: 01/07/2019
Page(s): 205-209

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