Optimal Predictive analytics of Pima Diabetics using Deep Learning

Abstract

An intelligent predictive model using deep learning is proposed to predict the patient risk factor and severity of diabetics using conditional data set. The model involves deep learning in the form of a deep neural network which helps to apply predictive analytics on the diabetes data set to obtain optimal results. The existing predictive models is used to predict the severity and the risk factor of the diabetics based on the data which is processed. In our case Firstly, a feature selection algorithm is run for the selection process. Secondly, the deep learning model has a deep neural network which employs a Restricted Boltzmann Machine (RBM) as a basic unit to analyse the data by assigning weights to the each branch of the neural network. This deep neural network, coded on python, will help to obtain numeric results on the severity and the risk factor of the diabetics in the data set. At the end, a comparative study is done between the implementation of this model on type 1 diabetes mellitus, Pima Indians diabetes and the Rough set theory model. The results add value to additional reports because the number of studies done on diabetes using a deep learning model is few to none. This will help to predict diabetes with much more precision as shown by the results obtained. characteristic