Predicting Cognitive Impairment and Dementia: A Machine Learning Approach.

ABSTRACT:

Background

Efforts to identify important risk factors for cognitive impairment and dementia have to date mostly relied on meta-analytic strategies. A comprehensive empirical evaluation of these risk factors within a single study is currently lacking.

Objective

We used a combined methodology of machine learning and semi-parametric survival analysis to estimate the relative importance of 52 predictors in forecasting cognitive impairment and dementia in a large, population-representative sample of older adults.

Methods

Participants from the Health and Retirement Study (N = 9,979; aged 50-98 years) were followed for up to 10 years (M = 6.85 for cognitive impairment; M = 7.67 for dementia). Using a split-sample methodology, we first estimated the relative importance of pre

SUBMITTER: Aschwanden D 

PROVIDER: S-EPMC7934087 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature