Explainable machine learning model for pre-frailty risk assessment in community-dwelling older adults.

ABSTRACT:

Background

Frailty in older adults is linked to increased risks and lower quality of life. Pre-frailty, a condition preceding frailty, is intervenable, but its determinants and assessment are challenging. This study aims to develop and validate an explainable machine learning model for pre-frailty risk assessment among community-dwelling older adults.

Methods

The study included 3141 adults aged 60 or above from the China Health and Retirement Longitudinal Study. Pre-frailty was characterized by one or two criteria from the physical frailty phenotype scale. We extracted 80 distinct features across seven dimensions to evaluate pre-frailty risk. A model was constructed using recursive feature elimination and a stacking-CatBoost distillation module on 80% of the sample and valid

SUBMITTER: Du C 

PROVIDER: S-EPMC11671213 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature