Dementia prediction in the general population using clinically accessible variables: a proof-of-concept study using machine learning. The AGES-Reykjavik study.

ABSTRACT:

Background

Early identification of dementia is crucial for prompt intervention for high-risk individuals in the general population. External validation studies on prognostic models for dementia have highlighted the need for updated models. The use of machine learning in dementia prediction is in its infancy and may improve predictive performance. The current study aimed to explore the difference in performance of machine learning algorithms compared to traditional statistical techniques, such as logistic and Cox regression, for prediction of all-cause dementia. Our secondary aim was to assess the feasibility of only using clinically accessible predictors rather than MRI predictors.

Methods

Data are from 4,793 participants in the population-based AGES-Reykjavik Study without

SUBMITTER: Twait EL 

PROVIDER: S-EPMC10463542 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature