Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)

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Result-Based Re-computation for Chronic Kidney Disease Prediction Using SVM Classification

Authors
P. Suresh Babu1, *, C. Madhuvarshni1, P. V. Jeyasree1, L. S. S. Jeyaroshini1, P. Deivandran2
1Information Technology, Velammal College of Engineering and Technology, Madurai, Tamilnadu, India
2Information Technology, Velammal Institute of Technology, Chennai, Tamilnadu, India
*Corresponding author. Email: psb@vcet.ac.in
Corresponding Author
P. Suresh Babu
Available Online 9 November 2023.
DOI
10.2991/978-94-6463-252-1_21How to use a DOI?
Keywords
Chronic Kidney; Support Vector Machine Classification; Machine Learning; RBR
Abstract

A crucial area of operation for cognitive intelligence systems is medical therapy. Across a wide range of health datasets, machine learning algorithms produce rapid disease prediction with excellent accuracy. A supervised machine learning approach for classification and regression applications is the Support Vector Machine (SVM). Error-Tolerant is the most difficult part of the SVM implementation. When utilizing an SVM in people’s safety applications, where a change in the classification result is impermissible, this is an actual problem. In this proposed system, ResultBasedRe-computation (RBR) is used as a productive approach to protect SVMs from errors.RBR is a useful method for protecting SVMs against kernel function faults. The constraints that affect the SVM result are re-computed for effective fault tolerance based on the observation from the classification. Other machine learning classifier’s results were also compared and it was found that the RBR system with SVM gave the highest accuracy.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)
Series
Advances in Engineering Research
Publication Date
9 November 2023
ISBN
978-94-6463-252-1
ISSN
2352-5401
DOI
10.2991/978-94-6463-252-1_21How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - P. Suresh Babu
AU  - C. Madhuvarshni
AU  - P. V. Jeyasree
AU  - L. S. S. Jeyaroshini
AU  - P. Deivandran
PY  - 2023
DA  - 2023/11/09
TI  - Result-Based Re-computation for Chronic Kidney Disease Prediction Using SVM Classification
BT  - Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)
PB  - Atlantis Press
SP  - 177
EP  - 189
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-252-1_21
DO  - 10.2991/978-94-6463-252-1_21
ID  - SureshBabu2023
ER  -