Prediction of Chronic Kidney Disease using Deep Belief Network

Abstract

This paper is proposed to develop an intelligent system that utilizes medical data analysis to predict and diagnose chronic kidney disease (CKD) at an early stage, with improved performance and accuracy compared to existing methods. As Chronic kidney disease (CKD) is still a health concern despite advances in surgical care and treatment. CKD’s growth in recent years has gained much interest from researchers around the world in developing high-performance methods for diagnosis, treatment and preventive therapy. Improved performance can be accomplished by learning the features that are in the concern of the problem. In addition to the clinical examination, analysis of the medical data for the patients can help the health care partners to predict the disease in early stage. Although there are many tries to build intelligent systems to predict the CKD by analysis the health data, the performance of these systems still need enhancement. Keywords - Categorical Cross-entropy, Chronic kidney Disease, Deep Belief Network, Deep Learning, Softmax classifier.