Prediction of Chronic and Non- Chronic Kidney disease using Modified DBN with Map and Reduce Framework

Abstract

Modern medical information comes in the form of an enormous volume of data that is challenging to maintain using conventional methods. The advancement of big data in the medical and basic healthcare societies is facilitated by precision medical data research, which focuses on comprehending early illness, patient healthcare facilities, and providers. It concentrates primarily on anticipating and discovering direct analysis of some of the substantial health effects that have increased in numerous countries. The existing health industry cannot retrieve detailed information from the chronic disease directory. The advancement of CKD (chronic kidney disease) and the methods used to identify the disease is a difficult task that can lower the cost of diagnosis. In this research, a modified MapReduce and pruning layer-based classification model using the deep belief network (DBN) and the dataset used as CKD were acquired from the UCI repository of machine learning. We have utilized the full potentiality of the DBNs by deploying deep learning methodology to establish better classification of the patient's kidney. Finally, data will be trained and classified using the classification layer and the quality will be compared to the existing method.