AI 中文总结
该研究针对慢性肾病预测问题，运用带投票分类器的联邦学习，结合随机森林等算法为全局服务器选最佳模型，用GridSearchCV优化客户端模型性能，还引入可解释人工智能技术，其全局模型平均准确率达99%，助力早期CKD诊断和数据驱动医疗。
AI 中文摘要
慢性肾病（CKD）以肾功能逐渐丧失为特征，仍然是一项重大的公共卫生挑战。早期检测对于预防严重并发症和改善患者预后至关重要。本研究中，使用带有投票分类器的联邦学习（FL），利用临床数据集预测CKD，其中随机森林、AdaBoost和XGBoost用于比较并为全局服务器确定最佳拟合模型。此外，应用网格搜索交叉验证（GridSearchCV）在客户端优化模型性能。为提高模型透明度和可信度，纳入可解释人工智能（XAI）技术来解释预测机制。全局模型平均准确率为99%，凸显了可解释FL模型在支持早期CKD诊断及推进数据驱动医疗解决方案方面的潜力。
英文摘要
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.
Comments13 Pages, 5 Figures
