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

Journal: BMC medical informatics and decision making
Published Date: Aug 28, 2023

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.

Authors

  • Emma L Twait
    Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands.
  • Constanza L Andaur Navarro
    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands c.l.andaurnavarro@umcutrecht.nl.
  • Vilmunur Gudnason
    Faculty of Medicine, University of Iceland, Reykjavik, Iceland.
  • Yi-Han Hu
    Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Baltimore, MD, USA.
  • Lenore J Launer
    Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA.
  • Mirjam I Geerlings
    Julius Center for Health Sciences and Primary Care, UMC Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.

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