AI-driven diagnosis of Lassa fever: Evidence from Nigerian clinical records
The ensemble learning approach demonstrated high effectiveness in improving disease detection accuracy, making it a practical tool for early diagnosis and clinical decision support in resource-limited healthcare settings. Its deployment could significantly reduce misdiagnosis and enhance NTD surveillance in Nigeria.
Authors
- Nnamdi Okomba
- Adebimpe Esan (ORCID: https://orcid.org/0000-0002-4059-9519)
- David B. Olawade (ORCID: https://orcid.org/0000-0003-0188-9836)
- Nicholas Aderinto (ORCID: https://orcid.org/0000-0003-0004-7389)
- Afeez Adekunle Soladoye (ORCID: https://orcid.org/0000-0002-6349-5173)
- George Adejo
Institutions
- Arden University (GB)
- Federal University Oye Ekiti (NG)
- Ladoke Akintola University of Technology (NG)
Publication Details
- Journal
- Computational Biology and Chemistry
- Published
- 2025-08-08
- DOI
- https://doi.org/10.1016/j.compbiolchem.2025.108627
- Citations
- 2
- Primary Topic
- Viral Infections and Outbreaks Research
- Type
- article
- Field-Weighted Citation Impact
- 2.79