ISSN :2582-9793
Md Arifuzzaman, Iftekhar Ahmed, Md. Jalal Uddin Chowdhury, Shadman Sakib, Mohammad Shoaib Rahman, Md. Ebrahim Hossain and Shakib Absar
Adv. Artif. Intell. Mach. Learn., 5 (1):3425-3445
Md Arifuzzaman : Leading University Sylhet Bangladesh
Iftekhar Ahmed : Leading University Sylhet Bangladesh
Md. Jalal Uddin Chowdhury : Leading University Sylhet Bangladesh
Shadman Sakib : Department of Information Systems University of Maryland
Mohammad Shoaib Rahman : Leading University Sylhet Bangladesh
Md. Ebrahim Hossain : Leading University Sylhet Bangladesh
Shakib Absar : Leading University Sylhet Bangladesh
DOI: https://dx.doi.org/10.54364/AAIML.2025.51196
Article History: Received on: 10-Dec-24, Accepted on: 25-Mar-25, Published on: 24-Mar-25
Corresponding Author: Md Arifuzzaman
Email: arif_cse@lus.ac.bd
Citation: Md Arifuzzaman, Iftekhar Ahmed, Md. Jalal Uddin Chowdhury , Mohammad Shoaib Rahman, Md. Ebrahim Hossain, Apurbo Deb Nath , Shakib Absar. (2025). A Novel Ensemble-Based Deep Learning Model with Explainable AI for Accurate Kidney Disease Diagnosis. Adv. Artif. Intell. Mach. Learn., 5 (1 ):3425-3445
Chronic kidney disease (CKD) represents a significant global health challenge characterized by a progressive decline in renal function, leading to the accumulation of waste products and disruptions in fluid balance within the body. Given its pervasive impact on public health, there is a pressing need for effective diagnostic tools to enable timely intervention. Our study delves into the application of cutting-edge transfer learning models for the early detection of CKD. We carefully test the performance of several cutting-edge models, such as EfficientNetV2, InceptionNetV2, MobileNetV2, and the Vision Transformer (ViT) technique, using a large dataset that is available to the public. Remarkably, our analysis demonstrates superior accuracy rates, surpassing the 90% threshold with MobileNetV2 and achieving 91.5% accuracy with ViT. Moreover, to enhance predictive capabilities further, we integrate these individual methodologies through ensemble modeling, resulting in our ensemble model exhibiting a remarkable 96% accuracy in the early detection of CKD. This significant advancement holds immense promise for improving clinical outcomes and underscores the critical role of machine learning in addressing complex medical challenges.