A machine learning framework for classifying dementia risk in mild cognitive impairment: evidence from a Korean genome-wide association study cohort.

ABSTRACT:

Background

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that presents challenges for early detection and intervention. Mild cognitive impairment (MCI), a critical precursor of AD, progresses to dementia in a substantial proportion of individuals annually. Genetic factors, particularly single nucleotide polymorphisms (SNPs), play a key role in the pathogenesis of AD, as identified by genome-wide association studies (GWAS). Therefore, we aimed to develop and evaluate predictive models for classifying patients with MCI into high- and low-risk groups for dementia using SNP chip data and machine learning (ML) algorithms.

Methods

Using data from the Biobank Innovations for Chronic Cerebrovascular Disease with Alzheimer's Disease Study, we conducted a GWAS t

SUBMITTER: Cho M 

PROVIDER: S-EPMC12604216 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature