Optimizing stroke prediction using gated recurrent unit and feature selection in Sub-Saharan Africa
The GRU-based system demonstrated superior performance in stroke prediction, offering an efficient and scalable tool for healthcare. Future research should focus on integrating unstructured data, validating the model on diverse populations, and exploring hybrid architectures to enhance predictive accuracy.
Authors
- Ibrahim Adeyanju (ORCID: https://orcid.org/0000-0002-6163-2453)
- Mayowa Ojo Owolabi (ORCID: https://orcid.org/0000-0003-1146-3070)
- Onoja Matthew Akpa (ORCID: https://orcid.org/0000-0002-5201-512X)
- David B. Olawade (ORCID: https://orcid.org/0000-0003-0188-9836)
- Nicholas Aderinto (ORCID: https://orcid.org/0000-0003-0004-7389)
- Afeez A Soladoye
Institutions
- University of Ibadan (NG)
- University College Hospital, Ibadan (NG)
- Arden University (GB)
- Federal University Oye Ekiti (NG)
- Ladoke Akintola University of Technology (NG)
Publication Details
- Journal
- Clinical Neurology and Neurosurgery
- Published
- 2025-01-27
- DOI
- https://doi.org/10.1016/j.clineuro.2025.108761
- Citations
- 13
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 38.19
Funders
- National Institutes of Health