Chronic Kidney Disease Detection
- 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.
Cite this article
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 -