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Comparative Study of Chronic Kidney Disease Prediction Using Different Classification Techniques

  • Pritha TikarihaEmail author
  • Prashant Richhariya
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 34)

Abstract

There are many fields where data mining is effectively applicable like marketing retail e-business, which has made it noticeable to the alternate sectors also. One of such sector is healthcare. Healthcare sector has colossal data, but those data are not utilized in a productive way, which make it knowledge poor. Also they lack in a proficient tool which helps to discover the concealed relationship among the available data. This paper presents analysis on some data mining techniques particularly in chronic kidney diseases (CKDs). K-nearest neighbor (KNN), C4.5, support vector machine (SVM), and Naïve Bayes classification algorithm are applied on the same dataset. The experimental result implemented in Weka tool shows that the KNN algorithm gives more accurate result when contrasted with different algorithms.

Keywords

Classification KNN Naïve Bayes Chronic kidney diseases SVM C4.5 

References

  1. 1.
    Tiwari, B., Kumar, A.: Role based access control through on-demand classification of electronic health record. Int. J. Electron. Healthc. (IJEH) 8(1), 9–24 (2015)Google Scholar
  2. 2.
    Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V., Fotiadis, D.I.: Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J. 13, 8–17 (2015)Google Scholar
  3. 3.
    Baby, P.S., Vital, T.P.: Statistical analysis and predicting kidney diseases using machine learning algorithms. Int. J. Eng. Res. Technol. 4, (2015)Google Scholar
  4. 4.
    Lakshmi, K.R., Nagesh, Y., VeeraKrishna, M.: Performance comparison of three data mining techniques for predicting kidney dialysis survivability. Int. J. Adv. Eng. Technol. 7, 242–254 (2014)Google Scholar
  5. 5.
    Kusiak, A., Dixon, B., Shah, S.: Predicting survival time for kidney dialysis patients: a data mining approach. 311–327 (2005)Google Scholar
  6. 6.
    Vijayarani, S., Dhayanand, S.: Kidney disease prediction using SVM and ANN algorithms. Int. J. Comput. Bus. Res. 6, (2015)Google Scholar
  7. 7.
  8. 8.
  9. 9.
    Leung, R.K.K., Wang, Y., Ma, R.C., Luk, A.O., Lam, V., Ng, M.: Using a multi-staged strategy based on machine learning and mathematical modeling to predict genotype phenotype risk patterns in diabetic kidney disease: a prospective case–control cohort analysis. BMC Nephrol. vol. 14, (2013)Google Scholar
  10. 10.
    Sinha, P., Sinha, P.: Comparative study of chronic kidney disease prediction using SVM and KNN. Int. J. Eng. Res. Technol. 4, (2015)Google Scholar
  11. 11.
    Othman, M.F., Yau, T.M.S.: Comparison of different classification techniques using WEKA for breast cancer. In: Biomed 06, IFMBE Proceedings, vol. 15, pp. 520–523. (2007)Google Scholar
  12. 12.
    Bala, S., Kumar, K.: A literature review on kidney disease prediction using data mining classification technique. Int. J. Comput. Sci. Mob. Comput. 3, 960–967 (2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringChhatrapati Shivaji Institute of TechnologyDurgIndia

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