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

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

B.V. Ravindra (School of Information Science, Manipal University, Manipal, India), N. Sriraam (Center for Medical Electronics and Computing, M.S. Ramaiah Institute of Technogy, Bangalore, India) and Geetha Maiya (Department of CSE, Manipal University, Manipal, India)
Copyright: © 2015 |Pages: 7
DOI: 10.4018/IJBCE.2015010104
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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.
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3. Experimental Analysis

The medical data taken into account for study is obtained from Chennai Global Hospital as per the earlier study reported in (Shah et al., 2003) (NIH Publication). We had taken into account230 records for this analysis. 12-15 kidney dialysis parameters from each record, the highest influencing parameters are only taken into account for the intended analysis. Centered on the Nephrologist advice, the precedence level is obtained. For example, the sign of high-ranking precedence shows the need for transplantation of kidney. Table 1 depicts the span of limits to be taken into account for conclusion rendering (Ravindra B.V. et. al., 2014). The WEKA tool version-3.7.9 is used for conducting the experiment

Table 1.
Scale of attributes for conclusion forming

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