Analysis and Comparison of Clustering Techniques for Chronic Kidney Disease With Genetic Algorithm

Analysis and Comparison of Clustering Techniques for Chronic Kidney Disease With Genetic Algorithm

Sanat Kumar Sahu (Govt. K. P.G. College Jagdalpur Bastar, Jagdalpur, India) and A. K. Shrivas (Dr. C. V. Raman University Kota Bialspur (C.G.), Bilaspur, India)
Copyright: © 2018 |Pages: 10
DOI: 10.4018/IJCVIP.2018100102

Abstract

The purpose of this article is to weigh up the foremost imperative features of Chronic Kidney Disease (CKD). This study is based mostly on three cluster techniques like; K means, Fuzzy c-means and hierarchical clustering. The authors used evolutionary techniques like genetic algorithms (GA) to extend the performance of the clustering model. The performance of these three clusters: live parameter purity, entropy, and Adjusted Rand Index (ARI) have been contemplated. The best purity is obtained by the K-means clustering technique, 96.50%; whereas, Fuzzy C-means clustering received 93.50% and hierarchical clustering was the lowest at 92. 25%. After using evolutionary technique Genetic Algorithm as Feature selection technique, the best purity is obtained by hierarchical clustering, 97.50%, compared to K –means clustering, 96.75%, and Fuzzy C-means clustering at 94.00%.
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A lot of researchers have worked in the field of cluster analysis which is related to the various dataset and chronic diseases like cancer, heart, and other diseases.

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