An Approach for improving the Prediction of Chronic Kidney Disease using Machine learning

Abstract

According the 2010 global burden of disease study, Chronic Kidney Diseases (CKD) was ranked 18th in the list of causes of total no. of deaths worldwide. 10% of the population worldwide is affected by CKD. The prediction of CKD can become a boon for the population to predict the health. Various method and techniques are undergoing the research phase for developing the most accurate CKD prediction system. Using Machine learning techniques is the most promising one in this area due to its computing function and Machine learning rules. Existing Systems are working well in predicting the accurate result but still more attributes of data and complicity of health parameter make the root layer for the innovation of new approaches. This study focuses on a novel approach for improving the prediction of CKD. In our proposed system we will implement the deep learning algorithms like Deep Neural Network. Chronic kidney disease detection system using deep network is shown here. This system of deep network accepts disease-symptoms as input and it is trained according to various training algorithms. After the network is trained, this trained network system is used for detection of kidney disease in the human body.