Spatial Machine Learning for Exploring the Variability in Low Height‐For‐Age From Socioeconomic, Agroecological, and Climate Features in the Northern Province of Rwanda

Spatial Machine Learning for Exploring the Variability in Low Height‐For‐Age From Socioeconomic, Agroecological, and Climate Features in the Northern Province of Rwanda

Abstract Childhood stunting is a serious public health concern in Rwanda. Although stunting causes have been documented, we still lack a more in‐depth understanding of their local factors at a more detailed geographic level. We cross‐sectionally examined 615 height‐for‐age prevalence observations in...

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Bibliographic Details
Main Authors: Gilbert Nduwayezu, Clarisse Kagoyire, Pengxiang Zhao, Lina Eklund, Petter Pilesjo, Jean Pierre Bizimana, Ali Mansourian
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2024-09-01
Series:GeoHealth
Subjects:
height‐for‐age
spatial machine learning
explainable artificial intelligence
local nonlinear effect
Online Access:https://doi.org/10.1029/2024GH001027
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https://doi.org/10.1029/2024GH001027

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