A Novel Ensemble-Based Deep Learning Model with Explainable AI for Accurate Kidney Disease Diagnosis

TL;DR

This paper introduces an ensemble deep learning approach with explainability for early CKD detection, achieving over 96% accuracy by combining transfer learning models like EfficientNetV2, InceptionNetV2, MobileNetV2, and ViT.

Contribution

It presents a novel ensemble model that significantly improves CKD diagnosis accuracy using transfer learning and explainable AI techniques.

Findings

Ensemble model achieves 96% accuracy in CKD detection.

MobileNetV2 and ViT outperform individual models with over 90% accuracy.

Transfer learning models effectively diagnose CKD from public datasets.

Abstract

Chronic Kidney Disease (CKD) represents a significant global health challenge, characterized by the 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. Leveraging a comprehensive and publicly available dataset, we meticulously evaluate the performance of several state-of-the-art models, including EfficientNetV2, InceptionNetV2, MobileNetV2, and the Vision Transformer (ViT) technique. 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.