Machine learning-based estimation of the mild cognitive impairment stage using multimodal physical and behavioral measures.

ABSTRACT: Mild cognitive impairment (MCI) is a prodromal stage of dementia, and its early detection is critical for improving clinical outcomes. However, current diagnostic tools such as brain magnetic resonance imaging (MRI) and neuropsychological testing have limited accessibility and scalability. Using machine-learning models, we aimed to evaluate whether multimodal physical and behavioral measures, specifically gait characteristics, body mass composition, and sleep parameters, could serve as digital biomarkers for estimating MCI severity. We recruited 80 patients diagnosed with MCI and classified them into early- and late-stage groups based on their Mini-Mental State Examination scores. Participants underwent clinical assessments, including the Consortium to Establish a Registry for Alzheimer's

SUBMITTER: Park I 

PROVIDER: S-EPMC12511372 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature