Predicting mortality risk in Alzheimer's disease using machine learning based on lifestyle and physical activity.

ABSTRACT: Alzheimer's disease (AD), a progressive neurodegenerative disorder, significantly impacts patient survival, prompting the need for accurate prognostic tools. Lifestyle factors and physical activity levels have been identified as critical modifiable risk factors influencing AD outcomes, but their precise impact on mortality prediction remains understudied. This study aimed to employ machine learning (ML) techniques to predict mortality risk in AD patients, leveraging data on lifestyle and physical activity to enhance personalized care strategies and inform public health policies. We analyzed data from 53,231 participants collected from the National Health and Nutrition Examination Survey (NHANES, 2007-2020). Participants were stratified by AD symptom severity using Patient Health Questionna

SUBMITTER: Tang R 

PROVIDER: S-EPMC12289877 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature