用联邦学习+可解释AI预测慢性肾病，准确率达99%。
Explainable AI for Chronic Kidney Disease Prediction Using Simulated Federated Learning
- 联邦学习聚合多个客户端数据训练模型，避免数据集中化
- 全局模型平均准确率99%，显著提升早期诊断能力
- 结合可解释AI技术，帮助医生理解预测依据，适合医疗场景
慢性肾病（CKD）以肾功能渐进性丧失为特征，仍是重大公共卫生挑战。早期检测对预防严重并发症、改善患者预后至关重要。本研究采用联邦学习（FL）与投票分类器预测CKD，使用随机森林、AdaBoost和XGBoost在客户端比较并选出最优全局模型。同时，通过网格搜索（GridSearchCV）优化客户端模型性能。为增强模型透明度与可信度，引入可解释AI（XAI）技术解析预测机制。全局模型平均准确率达99%，表明可解释联邦学习在支持早期CKD诊断及推动数据驱动医疗解决方案方面具有巨大潜力。
原文摘要 · Abstract (English)
Chronic Kidney Disease (CKD), characterized by the gradual loss of kidney function, remains a significant public health challenge. Early detection is crucial for preventing severe complications and enhancing patient outcomes. In this study, Federated Learning (FL) with a VotingClassifier was used to predict CKD using a clinical dataset, where Random Forest, AdaBoost, and XGBoost were utilized to compare and identify the best-fitting model for the global server. Additionally, GridSearchCV was applied to optimize the models' performance on the client's side. To enhance model transparency and trustworthiness, explainable AI (XAI) techniques were incorporated to interpret the prediction mechanisms. The global model's average accuracy was 99%, highlighting the potential of interpretable FL models in supporting early CKD diagnosis and advancing data-driven healthcare solutions.
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