Machine learning insights on activities of daily living disorders in Chinese older adults.

Objective: 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.

Method: 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.

Result: 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.

Conclusion: 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.