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

Abstract

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

• 1)
  The primary question addressed by this study is: What modifiable risk factors can impact Activities of Daily Living (ADL) in older adults?
• 2)
  The main finding of this study is that specific daily activities, such as playing mahjong and engaging in outdoor activities, significantly reduce the risk of future ADL disorders in older adults. Additionally, a robust predictive model was developed using longitudinal data from 13,220 individuals, improving the accuracy of ADL disorder risk predictions.
• 3)
  The meaning of the finding is that incorporating behavioral interventions into community care strategies can effectively enhance the well-being of older adults by minimizing their risk of ADL dysfunction.