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Classification of Chronic Kidney Disease with Genetic Search Intersection Based Feature Selection Technique

  • Sanat Kumar SahuEmail author
  • Prem Kumar Chandrakar
Conference paper
  • 15 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1122)

Abstract

The objective of this study had reduced the identification time and to get best diagnosis model in Chronic Kidney Disease (CKD). The target was development of the strong and computationally proficient model for classification of CKD. This work had used four classification models like Naive Bayes, Multilayer Perceptron, OneR, Classification and Regression Tree (CART) to classify the CKD data set and compared the classification accuracy with all classifier. Similarly Feature Selection Technique (FST), ranking based namely Chi Squared AttributeEval (CSAE), One-R AttributeEval (ORAE) and Search based namely Genetic Search-J48 (GS-J48), Genetic Search-CART (GS-CART) have used. The contribution of this research work is to recognize and classify the CKD problem and propose a new FST namely Genetic Search Intersection Based Feature Selection Technique (GS-IBFST). The classifier offers higher accuracy with GS-IBFST compare to without FSTs and existing FSTs.

Keywords

Chronic Kidney Disease Genetic Search Intersection Based Feature Selection Technique Naive Bayes Multilayer Perceptron OneR Classification and Regression Tree (CART) 

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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Govt. Kaktiya P.G. CollegeJagdalpur, BastarIndia
  2. 2.Mahant Laxmi Narayan Das CollegeRaipurIndia

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