Forecasting acute childhood malnutrition in Kenya using machine learning and diverse sets of indicators
To the best of the authors' knowledge, this work is the first to use clinical indicators collected via DHIS2 to forecast acute malnutrition in childhood at the sub-county level in Kenya. This work represents a foundational step in developing a broader childhood malnutrition forecasting framework, capable of monitoring malnutrition trends and identifying impending malnutrition peaks across more than 80 low- and middle-income countries collecting similar DHIS2 datasets.
Authors
- Herbert B. Wanyonyi
- Rahul Dodhia (ORCID: https://orcid.org/0000-0003-3812-3906)
- Laura Ferguson (ORCID: https://orcid.org/0000-0001-8563-734X)
- Girmaw Abebe Tadesse (ORCID: https://orcid.org/0000-0002-2648-9102)
- Juan Lavista Ferres (ORCID: https://orcid.org/0000-0002-9654-3178)
- Samuel Mburu (ORCID: https://orcid.org/0000-0003-4028-6026)
- Shiphrah Kuria (ORCID: https://orcid.org/0000-0001-9062-5140)
- Caleb Robinson
- Samuel Murage
- Bistra Dilkina (ORCID: https://orcid.org/0000-0002-6784-473X)
Institutions
- University of Southern California (US)
- Microsoft (United States) (US)
- Amref Health Africa (KE)
- Ministry of Health (KE)
- Microsoft Research (United Kingdom) (GB)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-05-14
- DOI
- https://doi.org/10.1371/journal.pone.0322959
- Citations
- 1
- Primary Topic
- Child Nutrition and Water Access
- Type
- article
- Field-Weighted Citation Impact
- 1.12