Evaluating machine learning models for stroke prediction based on clinical variables
These findings underscore both the promise and current limitations of ML in stroke risk prediction and highlight the need for future research leveraging multi-modal datasets and advanced algorithmic strategies to enhance sensitivity and clinical utility.
Authors
- Olukayode Karunwi (ORCID: https://orcid.org/0000-0003-3334-9703)
- Stephen Ojo (ORCID: https://orcid.org/0000-0002-2383-621X)
- Thomas I. Nathaniel (ORCID: https://orcid.org/0000-0003-0954-5050)
- J. A. Wanliss (ORCID: https://orcid.org/0000-0002-3291-6529)
- Muhammad Sulaiman (ORCID: https://orcid.org/0000-0002-4040-6211)
- Patrick O. Akinwumi
Institutions
- University of South Carolina (US)
- University of New Hampshire (US)
- Anderson University - Indiana (US)
- Anderson University - South Carolina (US)
- Clemson University (US)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-09-12
- DOI
- https://doi.org/10.3389/fneur.2025.1668420
- Citations
- 5
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 6.77