Exploring machine learning algorithms to predict acute respiratory tract infection and identify its determinants among children under five in Sub-Saharan Africa
According to this study, children who didn't take vaccinations had weakened immune systems and were highly affected by ARIs in Sub-Saharan Africa. The random forest machine learning model provides greater predictive power for estimating acute respiratory infections and identifying risk factors. This leads to a recommendation for policy direction to reduce infant mortality in Sub-Saharan Africa.
Authors
- Sisay Maru Wubante (ORCID: https://orcid.org/0000-0001-9442-5074)
- Nebiyu Mekonnen Derseh (ORCID: https://orcid.org/0000-0003-1355-2763)
- Bezawit Melak Fente (ORCID: https://orcid.org/0009-0008-9574-2299)
- Tirualem Zeleke Yehuala (ORCID: https://orcid.org/0009-0007-3909-8825)
Institutions
- University of Gondar (ET)
Publication Details
- Journal
- Frontiers in Pediatrics
- Published
- 2024-11-20
- DOI
- https://doi.org/10.3389/fped.2024.1388820
- Citations
- 3
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 1.21