用集成深度学习模型提升肾病早期诊断准确率，达96%。
A Novel Ensemble-Based Deep Learning Model with Explainable AI for Accurate Kidney Disease Diagnosis
- 融合MobileNetV2、ViT等模型，构建可解释的集成诊断系统。
- 在公开数据集上实现96%准确率，优于单一模型。
- 适合医疗AI研究者与临床辅助诊断系统开发者参考。
慢性肾病（CKD）是全球重大健康挑战，表现为肾功能渐进性下降，导致代谢废物积聚和体液平衡紊乱。为实现早期干预，本研究探索前沿迁移学习模型在CKD早期检测中的应用。基于一个全面且公开的数据集，我们系统评估了EfficientNetV2、InceptionNetV2、MobileNetV2及视觉变换器（ViT）等多种先进模型。结果显示，MobileNetV2超过90%准确率，ViT达到91.5%。进一步通过集成建模融合各方法，所提集成模型在早期检测中实现96%准确率。该成果显著提升预测能力，对改善临床结局具有重要意义，凸显机器学习在复杂医学问题中的关键作用。
原文摘要 · Abstract (English)
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.
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