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Comparative study of machine learning methods for modeling associations between risk factors and future dementia cases

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Abstract

A substantial portion of dementia risk can be attributed to modifiable risk factors that can be affected by lifestyle changes. Identifying the contributors to dementia risk could prove valuable. Recently, machine learning methods have been increasingly applied to healthcare data. Several studies have attempted to predict dementia progression by using such techniques. This study aimed to compare the performance of different machine-learning methods in modeling associations between known cognitive risk factors and future dementia cases. A subset of the AGES-Reykjavik Study dataset was analyzed using three machine-learning methods: logistic regression, random forest, and neural networks. Data were collected twice, approximately five years apart. The dataset included information from 1,491 older adults who underwent a cognitive screening process and were considered to have healthy cognition at baseline. Cognitive risk factors included in the models were based on demographics, MRI data, and other health-related data. At follow-up, participants were re-evaluated for dementia using the same cognitive screening process. Various performance metrics for all three machine learning algorithms were assessed. The study results indicate that a random forest algorithm performed better than neural networks and logistic regression in predicting the association between cognitive risk factors and dementia. Compared to more traditional statistical analyses, machine-learning methods have the potential to provide more accurate predictions about which individuals are more likely to develop dementia than others.

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Notes

  1. The work performed for this study falls under the machine learning approach to logistic regression, but there is, however, also a reference to a previous publication [28] that applies logistic regression using traditional statistics.

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Funding

This work was supported by The Foundation of St. Josef’s Hospital in cooperation with The Icelandic Gerontological Research Center, National University Hospital of Iceland; Landspítali – University Hospital Research Fund; the Icelandic Gerontological Society; the Council on Aging in Iceland; Helga Jónsdóttir and Sigurliði Kristjánsson Memorial Fund and the Sustainability Institute and Forum (SIF) at Reykjavik University.

The AGES-Reykjavik Study received funding from the National Institutes of Health, Intramural Research Programs of the National Institute of Aging and the National Eye Institute (grant number ZIAEY00401); National Institutes of Health (contract number N01-AG-1–2100); the Icelandic Heart Association and the Icelandic Parliament.

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Authors and Affiliations

  1. Department of Psychology, Reykjavik University, Reykjavik, Iceland

    Vaka Valsdóttir, María K. Jónsdóttir & Brynja Björk Magnúsdóttir

  2. RHLÖ – Icelandic Gerontological Research Center, Landspítali University Hospital, Reykjavik, Iceland

    Vaka Valsdóttir & Milan Chang

  3. Mental Health Services, Landspitali University Hospital, Reykjavik, Iceland

    María K. Jónsdóttir & Brynja Björk Magnúsdóttir

  4. Laboratory of Epidemiology and Population Sciences, National Institute On Aging, National Institutes of Health (NIH), Bethesda, MD, USA

    Yi-Han Hu & Lenore J. Launer

  5. Faculty of Medicine, University of Iceland, Reykjavik, Iceland

    Vilmundur Gudnason

  6. The Icelandic Heart Association, Kopavogur, Iceland

    Vilmundur Gudnason

  7. Department of Engineering, Reykjavik University, Reykjavik, Iceland

    Hlynur Stefánsson

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  1. Vaka Valsdóttir
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  2. María K. Jónsdóttir
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Correspondence to Vaka Valsdóttir.

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Valsdóttir, V., Jónsdóttir, M.K., Magnúsdóttir, B.B. et al. Comparative study of machine learning methods for modeling associations between risk factors and future dementia cases. GeroScience 46, 737–750 (2024). https://doi.org/10.1007/s11357-023-01040-9

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