Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)

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Chronic Kidney Disease Detection

Authors
Jaya Jeswani1, *, Mohammed Multazim Ansari1, Rushikesh Durgade1, Alisha Fatima Ansari1
1Department of Information Technology, Xavier Institute of Engineering, University of Mumbai, Mumbai, Maharashtra, India
*Corresponding author. Email: jaya.j@xavier.ac.in
Corresponding Author
Jaya Jeswani
Available Online 1 May 2023.
DOI
10.2991/978-94-6463-136-4_2How to use a DOI?
Keywords
Kidney; Disease; Machine Learning; Image Processing; Chronic Kidney Disease
Abstract

The impact of technological advancement, particularly machine learning, on health can be seen in the efficient analysis of different chronic diseases that allows for more precise diagnosis and effective treatment. People aged 60 and above are most affected by kidney disease, a serious chronic condition linked to ageing, hypertension, and diabetes. Early diagnosis of CKD enables patients to receive immediate treatment, which slows the disease’s further development. This study employs the machine learning techniques of artificial neural networks, support vector machines, and k-Nearest Neighbour to identify CDK early. The significance of detecting these frequently fatal illnesses reflects the significance of AI. These four processes of image pre-processing, feature extraction, classification, and diagnosis are used to identify the type of disease. Convolution Neural Network (CNN), which has a number of prediction-based layers, is used for categorisation and image pre-processing to improve the image’s quality. At the very end, the user is encouraged to get a cure.

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 Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
Series
Advances in Computer Science Research
Publication Date
1 May 2023
ISBN
10.2991/978-94-6463-136-4_2
ISSN
2352-538X
DOI
10.2991/978-94-6463-136-4_2How 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  - Jaya Jeswani
AU  - Mohammed Multazim Ansari
AU  - Rushikesh Durgade
AU  - Alisha Fatima Ansari
PY  - 2023
DA  - 2023/05/01
TI  - Chronic Kidney Disease Detection
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 4
EP  - 10
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_2
DO  - 10.2991/978-94-6463-136-4_2
ID  - Jeswani2023
ER  -