Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspective.

ABSTRACT:

Background

This study aimed to identify long-term frailty trajectories among older adults (≥65) and construct interpretable prediction models to assess the risk of developing abnormal frailty trajectory among older adults and examine significant factors related to the progression of frailty.

Methods

This study retrospectively collected data from the Chinese Longitudinal Healthy Longevity and Happy Family Study between 2002 and 2018 (N = 4083). Frailty was defined by the frailty index. The whole study consisted of two phases of tasks. First, group-based trajectory modeling was used to identify frailty trajectories. Second, easy-to-access epidemiological data was utilized to construct machine learning algorithms including naïve bayes, logistic regression, decision tree, suppor

SUBMITTER: Wu Y 

PROVIDER: S-EPMC9700973 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature