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Chronic kidney disease (CKD) refers to the failure of the renal functionalities that leads to the deposition of wastes, electrolytes and other fluids in the body. It is very important to recognize the symptoms that cause the CKD and pathological blood and urine test indicates the key attributes. It is well fact that one has to undergo dialysis due to renal failure. The severity level of disease can be predicted as well as classified using appropriate computer aided quantitative tools. This specific study discusses the classification of chronic and non-chronic kidney disease NCKD using support vector machine (SVM) neural networks. The simulation study makes use of UCI repository CKD datasets with n=400. In order to train to train the attributes of kidney dialysis four cases were considered by including the nominal and numerical values. A radical basis kernel function was employed to train SVM. The performance of the proposed scheme is evaluated in terms of the sensitivity, specificity and classification accuracy. Results reveal an overall classification accuracy of 94.44% was obtained by combining 6 attributes. It can be concluded that the SVM based approach found to be a potential candidate for classification of CKD and NCKD.

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The influencing aspects for kidney dialysis such as creatinine, sodium, urea & potassium levels display a critical part in determining the persistence estimate of the patients as well as the need for undergoing kidney transplantation. Numerous efforts are been through to develop computerized choice making procedure for earlier persistence. This preliminary study finds the impact of significant parameters based on the precedence of parameters suggested by the doctors & using the k-Means algorithm. With this algorithm knowledge about the collaboration among several of those measured parameters and patient persistence. The clustering method finds critical parameter that assists in estimating the persistence period of the patients who is taking the dialysis treatment.