Individualized prediction of chronic kidney disease for the elderly in longevity areas in China: Machine learning approaches.
Individualized prediction of chronic kidney disease for the elderly in longevity areas in China: Machine learning approaches.
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DOI：
10.3389/fpubh.2022.998549
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发表时间：
2022
影响因子：
5.2
通讯作者：
Wu, Nina
中科院分区：
文献类型：
--
作者：
Su, Dai;Zhang, Xingyu;He, Kevin;Chen, Yingchun;Wu, Nina
慢性肾脏病（CKD）已成为全球范围内的主要公共卫生问题，并已造成巨大的社会和经济负担，特别是在发展中国家。此前没有研究使用机器学习（ML）方法结合纵向数据来预测中国老年人2年内发生CKD的风险。该研究基于健康老龄化和生物标志物队列研究（HABCS）数据库2012年基线调查和2014年随访调查中925名老年人的面板数据。开发了六种ML模型，即逻辑回归（LR）、套索回归、随机森林（RF）、梯度提升决策树（GBDT）、支持向量机（SVM）和深度神经网络（DNN），以预测2年（2014年）内老年人CKD的概率。决策曲线分析（DCA）提供了每个ML模型的结果和净收益的阈值概率范围。在HABCS 2014年调查的925名老年人中，289人（18.8%）患有CKD。与其他模型相比，LR、lasso回归、RF、GBDT和DNN的受试者工作曲线下面积（AUC）值无统计学意义（>0.7），SVM的预测性能最低（AUC = 0.633，p值= 0.057）。DNN的阳性预测值（PPV）最高（0.328），而LR的阳性预测值（PPV）最低（0.287）。DCA结果表明，在阈值0-0.03和0.37-0.40范围内，GBDT的净效益最大。在~0.03-0.10和0.26-0.30的阈值范围内，RF的净效益最大。年龄是RF和GBDT模型中最重要的预测变量。血尿素氮、血清白蛋白、尿酸、体重指数（BMI）、婚姻状况、日常生活活动能力（ADL）/工具性日常生活活动能力（IADL）和性别是预测老年CKD的重要因素。ML模型可以成功地捕获老年人CKD危险因素的线性和非线性关系。本研究建立的基于预测模型的决策支持系统可以帮助医务人员对老年人的健康状况进行早期发现和干预。
Chronic kidney disease (CKD) has become a major public health problem worldwide and has caused a huge social and economic burden, especially in developing countries. No previous study has used machine learning (ML) methods combined with longitudinal data to predict the risk of CKD development in 2 years amongst the elderly in China. This study was based on the panel data of 925 elderly individuals in the 2012 baseline survey and 2014 follow-up survey of the Healthy Aging and Biomarkers Cohort Study (HABCS) database. Six ML models, logistic regression (LR), lasso regression, random forests (RF), gradient-boosted decision tree (GBDT), support vector machine (SVM), and deep neural network (DNN), were developed to predict the probability of CKD amongst the elderly in 2 years (the year of 2014). The decision curve analysis (DCA) provided a range of threshold probability of the outcome and the net benefit of each ML model. Amongst the 925 elderly in the HABCS 2014 survey, 289 (18.8%) had CKD. Compared with the other models, LR, lasso regression, RF, GBDT, and DNN had no statistical significance of the area under the receiver operating curve (AUC) value (>0.7), and SVM exhibited the lowest predictive performance (AUC = 0.633, p-value = 0.057). DNN had the highest positive predictive value (PPV) (0.328), whereas LR had the lowest (0.287). DCA results indicated that within the threshold ranges of ~0–0.03 and 0.37–0.40, the net benefit of GBDT was the largest. Within the threshold ranges of ~0.03–0.10 and 0.26–0.30, the net benefit of RF was the largest. Age was the most important predictor variable in the RF and GBDT models. Blood urea nitrogen, serum albumin, uric acid, body mass index (BMI), marital status, activities of daily living (ADL)/instrumental activities of daily living (IADL) and gender were crucial in predicting CKD in the elderly. The ML model could successfully capture the linear and nonlinear relationships of risk factors for CKD in the elderly. The decision support system based on the predictive model in this research can help medical staff detect and intervene in the health of the elderly early.
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