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.
摘要：
被生物心理社会框架告知，我们的研究使用中国纵向健康长寿调查(CLHLS)数据集来检查年龄最大(80岁以上)人群的认知功能轨迹.采用K均值聚类，我们确定了两个潜在的群体：高稳定性（HS）和低稳定性（LS）。HS组保持满意的认知功能，而LS组表现出一贯的低功能。Lasso回归揭示了预测因素，包括社会经济地位，生物条件,心理健康,生活方式,心理,和行为因素。这种数据驱动的方法揭示了认知衰老模式，并为健康衰老提供了政策。我们的研究在这种情况下开创了非参数机器学习方法。
