{Reference Type}: Journal Article
{Title}: Late Life Cognitive Function Trajectory Among the Chinese Oldest-Old Population-A Machine Learning Approach.
{Author}: Hu J;Ye M;Xi J;
{Journal}: J Gerontol Soc Work
{Volume}: 0
{Issue}: 0
{Year}: 2024 Apr 8
{Factor}: 3.608
{DOI}: 10.1080/01634372.2024.2339982
{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.