Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)

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Integrative Deep Learning Framework for Accurate Real-Time Kidney Disease Detection

Authors
J. Joan Niveda1, *, R. Yogesh Rajkumar2
1Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India
2Department of Information Technology, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India
*Corresponding author. Email: joeneci.2109@gmail.com
Corresponding Author
J. Joan Niveda
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-654-8_26How to use a DOI?
Keywords
Chronic Kidney Disease; Logistic Regression; Real-Time Diagnosis; Clinical Parameters; AI in Healthcare; Kidney Disease Detection
Abstract

Chronic Kidney Disease (CKD) is a major global health problem which requires early and accurate detection to better clinical outcomes and lower charge on the healthcare system. In this research, we introduce a real time kidney disease detection system that uses a logistic regression model that is trained from vital clinical information such as age, blood pressure and albumin. The system is intended for immediate diagnostic feedback to provide health care professionals with an informative tool for timely decision making. Preprocessing is performed rigorously in order to maintain data integrity, and the model is integrated to a user-friendly framework enabling seamless adoption in clinical settings. Diagnostic accuracy and system reliability are demonstrated in real world testing and by qualitative feedback from healthcare providers. These results show the potential of the use of this AI driven solution to transform CKD diagnosis through a higher efficiency and accessibility in different healthcare settings. Future improvements are to extend the model’s reach with more data sources and more AI techniques for greater precision and interpretability.

Copyright
© 2026 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 Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
Series
Advances in Engineering Research
Publication Date
24 April 2026
ISBN
978-94-6239-654-8
ISSN
2352-5401
DOI
10.2991/978-94-6239-654-8_26How to use a DOI?
Copyright
© 2026 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  - J. Joan Niveda
AU  - R. Yogesh Rajkumar
PY  - 2026
DA  - 2026/04/24
TI  - Integrative Deep Learning Framework for Accurate Real-Time Kidney Disease Detection
BT  - Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
PB  - Atlantis Press
SP  - 304
EP  - 314
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-654-8_26
DO  - 10.2991/978-94-6239-654-8_26
ID  - Niveda2026
ER  -