Develop a diagnostic tool for dementia using machine learning and non-imaging features.

ABSTRACT:

Background

Early identification of Alzheimer's disease or mild cognitive impairment can help guide direct prevention and supportive treatments, improve outcomes, and reduce medical costs. Existing advanced diagnostic tools are mostly based on neuroimaging and suffer from certain problems in cost, reliability, repeatability, accessibility, ease of use, and clinical integration. To address these problems, we developed, evaluated, and implemented an early diagnostic tool using machine learning and non-imaging factors.

Methods and results

A total of 654 participants aged 65 or older from the Nursing Home in Hangzhou, China were identified. Information collected from these patients includes dementia status and 70 demographic, cognitive, socioeconomic, and clinical features. Logis

SUBMITTER: Wang H 

PROVIDER: S-EPMC9461143 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature