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Classification of Chronic Kidney Disease using Feature Selection Techniques

A. K. Shrivas1 , Sanat Kumar Sahu2

  1. Dept. of IT, Dr. C. V. Raman University, Bilaspur (C.G.), India.
  2. Dept. of Computer Science, Govt. Kaktiya P.G. College, Jagdalpur (C.G.), India.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-5 , Page no. 649-653, May-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i5.649653

Online published on May 31, 2018

Copyright © A. K. Shrivas, Sanat Kumar Sahu . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: A. K. Shrivas, Sanat Kumar Sahu, “Classification of Chronic Kidney Disease using Feature Selection Techniques,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.649-653, 2018.

MLA Style Citation: A. K. Shrivas, Sanat Kumar Sahu "Classification of Chronic Kidney Disease using Feature Selection Techniques." International Journal of Computer Sciences and Engineering 6.5 (2018): 649-653.

APA Style Citation: A. K. Shrivas, Sanat Kumar Sahu, (2018). Classification of Chronic Kidney Disease using Feature Selection Techniques. International Journal of Computer Sciences and Engineering, 6(5), 649-653.

BibTex Style Citation:
@article{Shrivas_2018,
author = {A. K. Shrivas, Sanat Kumar Sahu},
title = {Classification of Chronic Kidney Disease using Feature Selection Techniques},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2018},
volume = {6},
Issue = {5},
month = {5},
year = {2018},
issn = {2347-2693},
pages = {649-653},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2036},
doi = {https://doi.org/10.26438/ijcse/v6i5.649653}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i5.649653}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2036
TI - Classification of Chronic Kidney Disease using Feature Selection Techniques
T2 - International Journal of Computer Sciences and Engineering
AU - A. K. Shrivas, Sanat Kumar Sahu
PY - 2018
DA - 2018/05/31
PB - IJCSE, Indore, INDIA
SP - 649-653
IS - 5
VL - 6
SN - 2347-2693
ER -

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Abstract

Classification and features selection play very important role to develop robust and computationally efficient model. In this paper, we have compared different classification techniques for classification of chronic kidney disease data. Two supervised classification learning algorithms are used to develop classifiers as Multilayer Perceptron Network (MLPN) and Radial Base Function Network (RBFN). The main focus of this research work is to reduce the number of features using different feature selection technique. We have also used five different classification techniques for select the relevant feature subsets and improve the accuracy of the classification through the Feature Selection Technique (FST). The RBFN classifier achieved the highest average percentage of performance in terms of accuracy. The results shows that both classification techniques given satisfactory accuracy rate in each different selected feature subset.

Key-Words / Index Term

MLP, RBFN,CKD, Feature Selection Techniques

References

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