International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

AI-Driven Diagnosis of Chronic Kidney Disease Using Deep Learning Techniques

Peddinti NeerajaAssistant Professor, Department of Computer Applications, School of Computing, Mohan Babu University, Tirupathi, A.P., IndiaV.Harsha KiranPG Student, Department of Computer Applications, School of Computing, Mohan Babu University, Tirupathi, A.P., India

Vol 9 No 25 (2025): Volume 9, Special Issue of INSPIRE’25 April 2025 | Pages: 295-300

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 24-04-2025

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Abstract

Persistent Kidney Disorder may seem to be knocking at the door of every community; it carries along its nature of morbidity and mortality along with it and various issues leading to the deterioration of health. Detection is rarely easy due to the asymptomatic presentations at early stages. With luck, early diagnosis of CKD allows timely intervention to slow the disease down. Deep learning models could really help clinicians monitor such conditions since they can rapidly and accurately spot such conditions. This paper elaborates on the use of machine learning in the diagnosis of CKD. The dataset is retrieved from the deep learning repository of the University of California, Irvine (UCI).  The framework aims at patients with CKD diagnosed as a result of the disease and examines whether the patients need to be treated. Various deep learning engines such as CNN, MobileNet, VGG16 were trained based on the sufficient models for kidney diagnostics. Among these, random forest gives the best of all accuracies. An integrated model proposed by the evaluation of errors of these models combined logistic regression with random forests using a perceptron for enhanced accuracy. This approach can foster the possible application of more complex clinical data for effective disease diagnosis. 

Keywords

Machine-learning platform, UCI repository, CKD diagnosis, deep learning algorithms, CNN (Convolutional Neural Network), MobileNet, Kidney disease models


Citation of this Article

Peddinti Neeraja, & V.Harsha Kiran. (2025). AI-Driven Diagnosis of Chronic Kidney Disease Using Deep Learning Techniques. In proceeding of International Conference on Sustainable Practices and Innovations in Research and Engineering (INSPIRE'25), published by IRJIET, Volume 9, Special Issue of INSPIRE’25, pp 295-300. Article DOI https://doi.org/10.47001/IRJIET/2025.INSPIRE48

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