Late Life Cognitive Function Trajectory Among the Chinese Oldest-Old Population-A Machine Learning Approach

Authors

Jierong Hu, University of Macau
Minzhi Ye, The University of Texas Rio Grande ValleyFollow
Juan Xi, Akron University

Document Type

Article

Publication Date

10-2024

Abstract

Informed by the biopsychosocial framework, our study uses the Chinese Longitudinal Healthy Longevity Survey (CLHLS) dataset to examine cognitive function trajectories among the oldest-old (80+). Employing K-means clustering, we identified two latent groups: High Stability (HS) and Low Stability (LS). The HS group maintained satisfactory cognitive function, while the LS group exhibited consistently low function. Lasso regression revealed predictive factors, including socioeconomic status, biological conditions, mental health, lifestyle, psychological, and behavioral factors. This data-driven approach sheds light on cognitive aging patterns and informs policies for healthy aging. Our study pioneers non-parametric machine learning methods in this context.

Comments

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Gerontological Social Work on October 2024 available via https://doi.org/10.1080/01634372.2024.2339982

Recommended Citation

Hu, J., Ye, M., & Xi, J. (2024). Late Life Cognitive Function Trajectory Among the Chinese Oldest-Old Population—A Machine Learning Approach. Journal of Gerontological Social Work, 67(7), 955-975. https://doi.org/10.1080/01634372.2024.2339982

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Publication Title

Journal of Gerontological Social Work

DOI

10.1080/01634372.2024.2339982