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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, A. K. Shrivas
Copyright: © 2018 |Volume: 8 |Issue: 4 |Pages: 10
ISSN: 2155-6997|EISSN: 2155-6989|EISBN13: 9781522545743|DOI: 10.4018/IJCVIP.2018100102
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MLA

Sahu, Sanat Kumar, and A. K. Shrivas. "Analysis and Comparison of Clustering Techniques for Chronic Kidney Disease With Genetic Algorithm." IJCVIP vol.8, no.4 2018: pp.16-25. http://doi.org/10.4018/IJCVIP.2018100102

APA

Sahu, S. K. & Shrivas, A. K. (2018). Analysis and Comparison of Clustering Techniques for Chronic Kidney Disease With Genetic Algorithm. International Journal of Computer Vision and Image Processing (IJCVIP), 8(4), 16-25. http://doi.org/10.4018/IJCVIP.2018100102

Chicago

Sahu, Sanat Kumar, and A. K. Shrivas. "Analysis and Comparison of Clustering Techniques for Chronic Kidney Disease With Genetic Algorithm," International Journal of Computer Vision and Image Processing (IJCVIP) 8, no.4: 16-25. http://doi.org/10.4018/IJCVIP.2018100102

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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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