Predicting incident dementia in cerebral small vessel disease: comparison of machine learning and traditional statistical models.

ABSTRACT:

Background

Cerebral small vessel disease (SVD) contributes to 45% of dementia cases worldwide, yet we lack a reliable model for predicting dementia in SVD. Past attempts largely relied on traditional statistical approaches. Here, we investigated whether machine learning (ML) methods improved prediction of incident dementia in SVD from baseline SVD-related features over traditional statistical methods.

Methods

We included three cohorts with varying SVD severity (RUN DMC, n = 503; SCANS, n = 121; HARMONISATION, n = 265). Baseline demographics, vascular risk factors, cognitive scores, and magnetic resonance imaging (MRI) features of SVD were used for prediction. We conducted both survival analysis and classification analysis predicting 3-year dementia risk.

SUBMITTER: Li R 

PROVIDER: S-EPMC10428032 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature