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)


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.


Classification KNN Naïve Bayes Chronic kidney diseases SVM C4.5 


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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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