Chronic kidney disease (CKD) is a major public health concern with rising prevalence and huge costs associated with dialysis and transplantation. Early prediction of CKD can reduce the patient's risk of CKD progression to end-stage kidney failure. Artificial intelligence offers more intelligent and expert healthcare services in disease diagnosis. In this work, a deep learning model is built using deep neural networks (DNN) with an adaptive moment estimation optimization function to predict early-stage CKD. The health care applications require interpretability over the predictions of the black-box model to build conviction towards the model's prediction. Hence, the predictions of the DNN-CKD model are explained by the local interpretable model-agnostic explainer (LIME). The diagnostic patient data is trained on five layered DNN with three hidden layers. Over the unseen data, the DNN-CKD model yields an accuracy of 98.75% and a roc_auc score of 98.86% in detecting CKD risk. The explanation revealed by the LIME algorithm echoes the influence of each feature on the prediction made by the DNN-CKD model over the given CKD data. With its interpretability and accuracy, the proposed system may effectively help medical experts in the early diagnosis of CKD.

中文翻译：

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用于预测早期慢性肾病的可解释深度学习模型

慢性肾病 (CKD) 是一个主要的公共卫生问题，其患病率不断上升，并且与透析和移植相关的费用也很高。早期预测 CKD 可以降低患者 CKD 进展为终末期肾衰竭的风险。人工智能在疾病诊断方面提供更加智能、专业的医疗服务。在这项工作中，使用具有自适应矩估计优化功能的深度神经网络 (DNN) 构建了深度学习模型来预测早期 CKD。医疗保健应用程序需要对黑盒模型的预测具有可解释性，以建立对模型预测的信心。因此，DNN-CKD 模型的预测由局部可解释模型不可知解释器 (LIME) 来解释。诊断患者数据在具有三个隐藏层的五层 DNN 上进行训练。在未见过的数据上，DNN-CKD 模型在检测 CKD 风险方面的准确率为 98.75%，roc_auc 得分为 98.86%。LIME 算法揭示的解释呼应了每个特征对 DNN-CKD 模型对给定 CKD 数据做出的预测的影响。凭借其可解释性和准确性，该系统可以有效帮助医学专家进行 CKD 的早期诊断。