A Comparative Analysis of Different Machine Learning Classification Algorithms for Predicting Chronic Kidney Disease

Authors

  • Sharma B Dept. of Computer Engineering, Government College of Engineering and Technology, Jammu and 181122, India
  • Gandotra S Dept. of Computer Engineering, Government College of Engineering and Technology, Jammu and 181122, India
  • Sharma U Dept. of Computer Engineering, Government College of Engineering and Technology, Jammu and 181122, India
  • Thakur R Dept. of Computer Engineering, Government College of Engineering and Technology, Jammu and 181122, India
  • Mahajan A Dept. of Computer Engineering, Government College of Engineering and Technology, Jammu and 181122, India

DOI:

https://doi.org/10.26438/ijcse/v7i6.813

Keywords:

CKD, Machine Learning, Logistic Regression, Support Vector Machine, Random Forest

Abstract

Chronic kidney disease (CKD) is a condition characterized by a gradual loss of kidney function over time. It includes risk of cardiovascular disease and end-stage renal disease. In this paper, we use Machine Learning approach for predicting CKD. In this paper, we present a comparative analysis of seven different machine learning algorithms. This study starts with twenty-four parameters in addition to the class attribute and twenty five percent of the data set is used to test the predictions. Algorithms are trained using fivefold cross-validation and performance of the system is assessed using classification accuracy, confusion matrix, specificity and sensitivity.

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Published

2019-06-30
CITATION
DOI: 10.26438/ijcse/v7i6.813
Published: 2019-06-30

How to Cite

[1]
B. Sharma, S. Gandotra, U. Sharma, R. Thakur, and A. Mahajan, “A Comparative Analysis of Different Machine Learning Classification Algorithms for Predicting Chronic Kidney Disease”, Int. J. Comp. Sci. Eng., vol. 7, no. 6, pp. 8–13, Jun. 2019.

Issue

Vol. 7 No. 6 (2019): IJCSE June Edition

Section

Research Article

License

Copyright (c) 2019 Sharma B, Gandotra S, Sharma U, Thakur R, Mahajan A

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