Zhang Huanting, Zhou Wenhao, He Jianan, Liu Xingyou, Shen Jie
HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou 310018, China.
College of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China.
Exp Gerontol. 2024 Dec;198:112641. doi: 10.1016/j.exger.2024.112641. Epub 2024 Nov 26.
This study on the aged population in China first used a large-scale longitudinal survey database to explore how different life factors affect their ability to engage in daily activities. We select and integrate multiple machine models to obtain an excellent model for analyzing relationships. Based on the identified factors, our goal is to help them maintain a good daily life and quality of life.
We analyzed data from 13,220 older individuals participating in the China Longitudinal Health Longevity Survey (CLHLS) from 2002 to 2018. ADL was measured based on participants' self-reported results. Nine machine learning algorithms, including neural networks and an ensemble model, were employed with a 2/3 training and 1/3 testing split. Model performance was evaluated using the area under the curve (AUC), sensitivity, and specificity, while logistic regression assessed the relationship between lifestyle changes and ADL disorders.
The K-nearest neighbors (KNN) and decision tree algorithms showed the best performance, with AUCs of 0.8598 and 0.8322, respectively. Combining results from all models improved the AUC to 0.8619. Activities, such as playing mahjong, engaging in outdoor work, and reducing TV time, were linked to lower ADL decline, with greater participation in social activities and pet care also being beneficial.
Machine learning algorithms, especially ensemble models, can effectively identify older adults at risk for ADL disorders. Increased outdoor activity, social engagement, and dietary adjustments are associated with a decreased risk of ADL deterioration. TRANSLATIONAL SIGNIFICANCE.
本项针对中国老年人群体的研究首次使用大规模纵向调查数据库，以探究不同生活因素如何影响他们参与日常活动的能力。我们选择并整合多种机器学习模型，以获得一个用于分析各种关系的优秀模型。基于所确定的因素，我们的目标是帮助他们维持良好的日常生活和生活质量。
我们分析了2002年至2018年参与中国健康与养老追踪调查（CLHLS）的13220名老年人的数据。日常生活活动能力（ADL）是根据参与者的自我报告结果来衡量的。我们采用了包括神经网络和集成模型在内的九种机器学习算法，将数据按2/3用于训练、1/3用于测试进行划分。使用曲线下面积（AUC）、敏感性和特异性来评估模型性能，同时采用逻辑回归分析生活方式变化与ADL障碍之间的关系。
K近邻算法（KNN）和决策树算法表现最佳，AUC分别为0.8598和0.8322。综合所有模型的结果可将AUC提高到0.8619。打麻将、从事户外工作以及减少看电视时间等活动与较低的ADL下降相关，更多地参与社交活动和照顾宠物也有益处。
机器学习算法，尤其是集成模型，能够有效识别有ADL障碍风险的老年人。增加户外活动、社交参与以及饮食调整与ADL恶化风险降低相关。转化意义。
