Predicting malnutrition in PLWHIV using machine learning in gondar, Ethiopia
ML models offer a robust approach for predicting malnutrition risk in PLWHIV patients. The integration of these tools into routine care could enhance nutritional management, particularly in low-resource settings. Further studies are needed to confirm these findings and improve the deployment of the model in clinical settings.
Authors
- Tadele Chekol Maru
- Meron Asmamaw Alemayehu (ORCID: https://orcid.org/0000-0002-1852-0771)
- Nebebe Demis Baykemagn (ORCID: https://orcid.org/0009-0008-9403-6915)
- Agmasie Damtew Walle (ORCID: https://orcid.org/0000-0001-9583-5876)
- Tirualem Zeleke Yehuala (ORCID: https://orcid.org/0009-0007-3909-8825)
- Abraham Keffale Mengistu
- Andualem Enyew Gedefaw
Institutions
- Debre Berhan University (ET)
- University of Gondar (ET)
- Debre Markos University (ET)
Publication Details
- Journal
- BMC Public Health
- Published
- 2025-09-30
- DOI
- https://doi.org/10.1186/s12889-025-24139-4
- Citations
- 1
- Primary Topic
- Child Nutrition and Water Access
- Type
- article
- Field-Weighted Citation Impact
- 1.12
Funders
- University of Gondar