Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)

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Prediction of Chronic Kidney Disease using Machine Learning Techniques

Authors
G. Suresh Reddy1, *, Raja Vamsi Bandireddy2, Likhitha Koppula2, Pravallika Sree Vani Nidadavolu2, Navya Pagadala2
1Professor at VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India
2Student at VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India
*Corresponding author. Email: sureshreddy_g@vnrvjiet.in
Corresponding Author
G. Suresh Reddy
Available Online 21 December 2023.
DOI
10.2991/978-94-6463-314-6_13How to use a DOI?
Keywords
CKD; ESRD; XG boost; Ada boost; K - Nearest Neighbor method; Support Vector Machine; Gradient Boosting; Ensemble
Abstract

Utilizing machine learning calculations, this study plans to foresee ongoing kidney illness. A condition wherein the kidneys cannot dispose of poisons from the body is known as persistent kidney sickness. It is conceivably the deadliest disorder, and a wrong finding can achieve mortality. Chronic kidney disease (CKD) influences around 800 individuals for every million individuals (pmp), while end-stage renal disease (ESRD) influences 150–200 individuals for every million individuals (pmp). In India, 18,000 to 20,000 patients (or 10% of new ESRD cases) use dialysis. A hemodialysis treatment costs between $15 and $40, and erythropoietin adds $150 to $200 per month. The patient may not exhibit any symptoms during the disease’s early stages that would prevent it from becoming chronic. Several data mining and machine learning algorithms can aid the diagnosis of CKD. This study’s dataset was obtained from the UCI repository. Expectation strategies included the K-Nearest Neighbor method, Random Forest, Decision Tree, Support Vector Machine, Gradient Boosting, XG boost, Ada boost, and Ensemble. The characteristics of prediction and their correlation were examined from a medical perspective.

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 International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
21 December 2023
ISBN
10.2991/978-94-6463-314-6_13
ISSN
2589-4900
DOI
10.2991/978-94-6463-314-6_13How 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  - G. Suresh Reddy
AU  - Raja Vamsi Bandireddy
AU  - Likhitha Koppula
AU  - Pravallika Sree Vani Nidadavolu
AU  - Navya Pagadala
PY  - 2023
DA  - 2023/12/21
TI  - Prediction of Chronic Kidney Disease using Machine Learning Techniques
BT  - Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)
PB  - Atlantis Press
SP  - 128
EP  - 141
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-314-6_13
DO  - 10.2991/978-94-6463-314-6_13
ID  - Reddy2023
ER  -