Predictors of weight gain in under-five children with severe acute malnutrition: A machine learning analysis of a tertiary care hospital dataset
Tree-based ensemble methods showed the greatest potential for future clinical application. This analysis, using explainable ML models, provided valuable insights into the role of baseline anthropometry and the importance of feeding practices in attaining weight from hospital admission to discharge in children under five treated for SAM. This finding has important implications for future research on childhood feeding practices, developing personalized nutrition strategies, and community-based detection of children with SAM.
Authors
- Paraskevi Massara (ORCID: https://orcid.org/0000-0003-0906-0985)
- Daniëlla Brals
- Wieger P. Voskuijl (ORCID: https://orcid.org/0000-0001-5825-2831)
- Elena M. Comelli (ORCID: https://orcid.org/0000-0002-5201-5437)
- Shah Mohammad Fahim (ORCID: https://orcid.org/0000-0002-3627-202X)
- Lauren Erdman (ORCID: https://orcid.org/0000-0002-7106-2669)
- Robert H.J Bandsma
- Mustafa Mahfuz
- Tahmeed Ahmed
- Md. Ashraful Alam
- S.M.Tafsir Hasan
- Subhasish Das
Institutions
- Cincinnati Children's Hospital Medical Center (US)
- University of Toronto (CA)
- Cornell University (US)
- International Centre for Diarrhoeal Disease Research (BD)
- Hospital for Sick Children (CA)
- Amsterdam Institute for Global Health and Development (NL)
- Amsterdam University Medical Centers (NL)
- The Wilson Centre (CA)
- University of Amsterdam (NL)
Publication Details
- Journal
- Clinical Nutrition ESPEN
- Published
- 2026-03-13
- DOI
- https://doi.org/10.1016/j.clnesp.2026.103119
- Primary Topic
- Child Nutrition and Water Access
- Type
- article
- Field-Weighted Citation Impact
- 0.00