Predicting cognitive frailty in community-dwelling older adults: a machine learning approach based on multidomain risk factors.

Cognitive frailty (CF), a clinical syndrome involving both physical frailty (PF) and impaired cognition (IC), is associated with adverse health outcomes in older adults. This study aimed to identify key predictors of CF and develop a machine learning-based model for CF risk assessment using data from 2404 community-dwelling older adults in the Korean Frailty and Aging Cohort Study (2016-2017). PF was evaluated using Fried frailty phenotype, while IC was assessed using Mini-Mental State Examination (MMSE). Participants exhibiting at least one frailty phenotype and MMSE score ≤ 24 were classified as having CF. A comprehensive analysis encompassing sociodemographic, clinical, and health status characteristics was conducted. A machine learning approach incorporating recursive feature elimination and bootstrapping was employed to develop the prediction model. Among the diverse CF-associated characteristics, the machine learning-based model identified six optimal features (key predictors): motor capacity, education level, physical function limitation, nutritional status, balance confidence, and activities of daily living. The model demonstrated robust predictive performance, achieving an area under the curve of 84.34%, with high sensitivity, specificity, and accuracy. These findings underscore the importance of comprehensive health assessments for early CF detection and demonstrate the potential of predictive modeling in facilitating personalized interventions for at-risk older adults.