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Scientific article
English

Predicting cognitive impairment and dementia: a machine learning approach

Published inJournal of Alzheimer's Disease, vol. 75, no. 3, p. 717-728
Publication date2020
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 predictors using machine learning (random forest survival analysis), and we then used semi-parametric survival analysis (Cox proportional hazards) to estimate effect sizes for the most important variables...

Keywords
  • Aging
  • Cognitive impairment
  • Cox proportional hazard survival analysis
  • Dementia
  • Machine learning
  • Protective factors
  • Random forest survival analysis
  • Risk factors
Citation (ISO format)
ASCHWANDEN, Damaris et al. Predicting cognitive impairment and dementia: a machine learning approach. In: Journal of Alzheimer’s Disease, 2020, vol. 75, n° 3, p. 717–728. doi: 10.3233/JAD-190967
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ISSN of the journal1387-2877
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Creation12/10/2020 2:13:00 PM
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