Chen Jing, Ren Yifei, Ding Jie, Hu Qingqing, Xu Jiajia, Luo Jun, Wu Zhaowen, Chu Ting
School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People's Republic of China.
School of Nursing, Zhejiang Chinese Medical University, 548 Binwen Road, Binjiang District, Hangzhou, 310053, Zhejiang Province, People's Republic of China.
Sci Rep. 2025 May 9;15(1):16247. doi: 10.1038/s41598-025-01404-5.
The study aimed to develop a predictive model using machine learning algorithms, providing healthcare professionals with a novel tool for assessing disability risk in older adults. Data from the 2018 and 2020 waves of the China Health and Retirement Longitudinal Study were utilized, including 3,172 participants aged 65 years and older with no baseline disability. In this study, five machine learning algorithms were employed to construct risk assessment and prediction models for disability in older adults. The Shapley Additive Explanations method was applied to analyze the independent predictors of disability risk. In total, 695 participants (21.9%) were disabled during follow-up. Among the five machine learning models, prediction models constructed using random forest and extreme gradient boosting methods showed superior performance, achieving F1 scores of 0.92 and 0.86 and accuracies of 0.92 and 0.85, respectively. Key predictors of disability risk included self-rated health, education, sleep duration, alcohol consumption, depressive symptoms, hypertension, and arthritis. The Machine learning models for assessing and predicting disability risk in older adults, particularly those developed using RF and XGBoost algorithms, exhibited strong predictive capabilities. These findings highlight the potential of these models for practical application in clinical and public health settings, warranting further exploration and validation.
该研究旨在使用机器学习算法开发一种预测模型，为医疗保健专业人员提供一种评估老年人残疾风险的新工具。利用了中国健康与养老追踪调查2018年和2020年两轮的数据，其中包括3172名65岁及以上且无基线残疾的参与者。在本研究中，采用了五种机器学习算法来构建老年人残疾风险评估和预测模型。应用夏普利加性解释方法分析残疾风险的独立预测因素。在随访期间，共有695名参与者（21.9%）出现残疾。在五个机器学习模型中，使用随机森林和极端梯度提升方法构建的预测模型表现出卓越的性能，F1分数分别为0.92和0.86，准确率分别为0.92和0.85。残疾风险的关键预测因素包括自评健康状况、教育程度、睡眠时间、饮酒情况、抑郁症状、高血压和关节炎。评估和预测老年人残疾风险的机器学习模型，特别是使用随机森林和XGBoost算法开发的模型，表现出强大的预测能力。这些发现凸显了这些模型在临床和公共卫生环境中实际应用的潜力，值得进一步探索和验证。
