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

Ravindra, B V and Sriraam, N and Geetha, M (2015) Finding Impact of Precedence based Critical Attributes in Kidney Dialysis Data Set using Clustering Technique. International Journal of Biomedical and Clinical Engineering, 4 (1). pp. 44-50.

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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

Item Type: Article
Uncontrolled Keywords: CKD, Dialysis, Hemodialysis, K-means Clustering, Persistence
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Information Sciences > MCIS Manipal
Depositing User: MIT Library
Date Deposited: 11 Apr 2016 16:10
Last Modified: 11 Apr 2016 16:10
URI: http://eprints.manipal.edu/id/eprint/145826

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