School of Social Work Faculty Publications and Presentations

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

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.

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© 2024 Taylor & Francis Group, LLC

Publication Title

Journal of gerontological social work

DOI

10.1080/01634372.2024.2339982

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