High-Performance Classification of Mpox Symptoms Using Support Vector Classifier and Quadratic Discriminant Analysis
These findings demonstrate the strong potential of machine learning classifiers for detecting Mpox based on clinical features. Incorporating these models into healthcare systems could significantly enhance early case detection, improve clinical decision-making, and bolster disease surveillance. Future research should focus on prospective validation of these ML classifiers in real-world clinical environments.
Authors
- Kolapo Oyebola (ORCID: https://orcid.org/0000-0002-1003-2570)
- Michael Olufemi
- Funmilayo C. Ligali (ORCID: https://orcid.org/0009-0007-6114-6715)
- Shalom C Okoli
Institutions
- Mountain Top University (NG)
- Nigerian Institute of Medical Research (NG)
Publication Details
- Journal
- medRxiv
- Published
- 2026-02-22
- DOI
- https://doi.org/10.64898/2026.02.12.26346046
- Primary Topic
- Poxvirus research and outbreaks
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Bill and Melinda Gates Foundation
- African Academy of Sciences
- National Institutes of Health
- Fogarty International Center