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Predicting cognitive impairment and dementia: a machine learning approach

Aschwanden, Damaris
Terracciano, Antonio
Sutin, Angelina R.
Brown, Justin
Allemand, Mathias
Published in Journal of Alzheimer's Disease. 2020, vol. 75, no. 3, p. 717-728
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 AgingCognitive impairmentCox proportional hazard survival analysisDementiaMachine learningProtective factorsRandom forest survival analysisRisk factors
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Research group Méthodologie et analyse des données (MAD)
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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 https://archive-ouverte.unige.ch/unige:147026

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Deposited on : 2021-01-08

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