Accurate and robust prediction of Amyloid-β brain deposition from plasma biomarkers and clinical information using machine learning
The random forest, support vector machine and multilayer perceptron methods can accurately predict brain Aβ status using plasma biomarkers, genotype, and clinical information. The method generalizes well to an independent dataset and can be reduced to using only five features without losing much accuracy, thus providing an inexpensive alternative to PET imaging.
Authors
- Andrew J. Doig (ORCID: https://orcid.org/0000-0003-0346-2270)
- Fumie Costen (ORCID: https://orcid.org/0000-0003-1582-1664)
- Petroula Proitsi (ORCID: https://orcid.org/0000-0002-2553-6974)
- Sofia Michopoulou
- Jiayuan Xu
Institutions
- Queen Mary University of London (GB)
- King's College London (GB)
- Southampton General Hospital (GB)
- University Hospital Southampton NHS Foundation Trust (GB)
- University of Manchester (GB)
Publication Details
- Journal
- Frontiers in Aging Neuroscience
- Published
- 2025-08-18
- DOI
- https://doi.org/10.3389/fnagi.2025.1559459
- Citations
- 1
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.48
Funders
- U.S. Department of Defense
- Alzheimer's Association
- Bristol-Myers Squibb
- Eli Lilly and Company
- Pfizer
- Biogen
- University of Southern California
- Meso Scale Diagnostics
- Alzheimer's Disease Neuroimaging Initiative
- BioClinica
- Novartis Pharmaceuticals Corporation
- Northern California Institute for Research and Education
- Eisai
- Servier
- H. Lundbeck A/S
- IXICO
- National Institutes of Health
- Genentech
- Canadian Institutes of Health Research