Predicting the Chronic Kidney Disease using Various Classifiers

Kidney is the most important organ in a human body. But now-a-days Chronic Kidney Disease (CKD) is the most common problem for the people. Today good many people die because of Chronic Kidney Disease. This CKD is the most common and a serious disease in the world. The gradual loss of function of a kidney is also called Chronic Kidney Disease. The affected kidney is measured on Glomerular Filtration Rate (GFR). If the GFR rate is high, the kidney is in a good condition and if the GFR rate is very low, the kidney is affected. In this paper, we have tried the feature selection method which is used to reduce the attributes and select the more essential attributes only. We can classify the data, using four classifiers namely JRip, SMO, Naive Bayes and IBK. Finally we can compare the results of reduced attribute dataset and original dataset result using these four classifiers. Thus we can find the correct classifier and the best classifier. The classification is the most important part of the processes and it is done using data mining technique based on the machine learning. The classification can be used to predict group membership for data instances.