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

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


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.


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


  1. 1.
    Subasi, A., Alickovic, E., Kevric, J.: Diagnosis of chronic kidney disease by using random forest. In: 2017 Proceedings of the International Conference on Medical and Biological Engineering, pp. 589–594 (2017)Google Scholar
  2. 2.
    Polat, H., Danaei Mehr, H., Cetin, A.: Diagnosis of chronic kidney disease based on support vector machine by feature selection methods. J. Med. Syst. 41(4), 55 (2017)CrossRefGoogle Scholar
  3. 3.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2012)zbMATHGoogle Scholar
  4. 4.
    Pujari, A.: Data Mining Techniques. University Press, Hyderabad (2013)Google Scholar
  5. 5.
    Jantawan, B., Tsai, C.: A comparison of filter and wrapper approaches with data mining techniques for. Int. J. Innov. Res. Comput. Commun. Eng. 2, 4501–4508 (2014)Google Scholar
  6. 6.
    Yildirim, P.: Filter based feature selection methods for prediction of risks in hepatitis disease. Int. J. Mach. Learn. Comput. 5, 258–263 (2015)CrossRefGoogle Scholar
  7. 7.
    Hall, M.: Correlation-based feature selection for machine learning. Methodology 21i195-i20, pp. 1–5 (1999)Google Scholar
  8. 8.
    Liu, H., Setiono, R., Science, C., Ridge, K.: Chi2: Feature Selection, pp. 388–391 (1995)Google Scholar
  9. 9.
    Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Mach. Learn. 11, 63–91 (1993)CrossRefGoogle Scholar
  10. 10.
    Shrivas, A.K., Sahu, S.K., Hota, H.S.: Classification of chronic kidney disease with proposed union based feature selection technique, pp. 503–507 (2018)Google Scholar
  11. 11.
    Charleonnan, A., et al.: Predictive analytics for chronic kidney disease using machine learning techniques. In: 2016 Management and Innovation Technology International Conference, pp. MIT-80–MIT-83 (2016).
  12. 12.
    Kunwar, V., Chandel, K., Sabitha, A.S., Bansal, A.: Chronic kidney disease analysis using data mining classification. In: 2016 6th International Conference-Cloud System and Big Data Engineering (Confluence), pp. 300–305. IEEE (2016).
  13. 13.
    Boukenze, B., Haqiq, A., Mousannif, H.: Predicting chronic kidney failure disease using data mining techniques, vol. 397 (2017)Google Scholar
  14. 14.
    Russell, S., Norvig, P.: Artificial Intelligence A Modern Approach. Series in Artificial Intelligence. Prentice Hall, Upper Saddle River (2003)zbMATHGoogle Scholar
  15. 15.
    Haykin, S.: Neural Networks and Learning Machines. Pearson Prentice Hall, Upper Saddle River (2008). 936 pLinks 3Google Scholar
  16. 16.
    Nasa, C., Suman, S.: Evaluation of different classification techniques for WEB data. Int. J. Comput. Appl. 52, 34–40 (2012)Google Scholar
  17. 17.
    Alam, F., Pachauri, S.: Comparative study of J48, Naive Bayes and One-R classification technique for credit card fraud detection using WEKA. Adv. Comput. Sci. Technol. 10, 1731–1743 (2017)Google Scholar
  18. 18.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation (1985)Google Scholar
  19. 19.
    Sivanandam, S.N., Deepa, S.N.: Principles of Soft Computing. Wiley, Hoboken (2014)Google Scholar
  20. 20.
    Novakovic, J., Strbac, P., Bulatovic, D.: Toward optimal feature selection using ranking methods and classification algorithms. Yugosl. J. Oper. Res. 21, 119–135 (2011)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Karegowda, A.G., Manjunath, A., Jayaram, M.: Application of genetic algorithm optimized neural network connection weights for medical diagnosis of PIMA Indians diabetes. Int. J. Soft Comput. 2, 15–23 (2011)CrossRefGoogle Scholar
  22. 22.
    Ashraf, M., Chetty, G., Tran, D.: Feature selection techniques on thyroid, hepatitis, and breast cancer datasets. Int. J. Data Min. Intell. Inf. Technol. Appl. 3, 1–8 (2013)Google Scholar
  23. 23.
    Arun Kumar, C., Sooraj, M.P., Ramakrishnan, S.: A comparative performance evaluation of supervised feature selection algorithms on microarray datasets. Procedia Comput. Sci. 115, 209–217 (2017)CrossRefGoogle Scholar
  24. 24.
    Witten, I.H., Frank, E., Hall, M.A.: Data Mining. Morgan Kaufmann series in data management systems (2011).;2-c
  25. 25.
    UCI Machine Learning Repository of machine learning databases (2015). Accessed 1 Jan 2016
  26. 26.
    Machine Learning Group at the University of Waikato.

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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