Early Prediction of Chronic Kidney Disease Using Deep Belief Network

Abstract

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. This Paper proposes an intelligent classification and prediction model. It uses modified Deep Belief Network (DBN) as classification algorithm to predict the kidney related diseases and the Softmax as activation function and the Categorical Cross-entropy as a loss function. The evaluation of the proposed model shows that the model can predict the CKD with accuracy 98.5% and sensitivity 87.5 % comparing with existing models. Result analysis proves that using advanced deep learning techniques is beneficial for clinical decision making and can aid in early prediction of CKD and its related phases that reduces the progression of the kidney damage.