Finding Impact of Precedence based Critical Attributes in Kidney Dialysis Data Set using Clustering Technique

Abstract

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.

Classification of non-chronic and chronic kidney disease using SVM neural networks

“…The application of appropriate feature selection technique was reported in the details for chronic kidney disease diagnosis [19]. Ravindra et al, have reported the importance of attribute selection for kidney dialysis survival prediction by making use of k-means clustering techniques [20], [21].…”