Machine learning techniques to model child low height-for-age in the Northern Province of Rwanda: The role of climatological and environmental factors and their interactions

Objective: Childhood stunting is a significant health issue in Rwanda, particularly within the Northern Province.While demographic and socio-economic factors have been more extensively studied, the impact of environmental and climatic factors on stunting prevalence has received less attention.This study aimed to determine if these factors could be used to better predict localized variations in height-for-age z-scores (HAZ).Study design: A population-based, cross-sectional study.Methods: Data were collected on child and maternal characteristics, household socioeconomic status, and environmental predictors.An eXtreme Gradient Boosting (XGBoost) algorithm was used, complemented by GeoShapley for spatial analyses, to explain the spatial variability between low height-for-age and its risk factors.Results: The model performed well, with the coefficient of determination (R2) value of 0.83, the root mean standardized error (RMSE) of 0.13, and the mean absolute error (MAE) of 0.10.Key predictors of HAZ included rainfall, childcare practices, food insecurity, elevation, and soil fertility.Considering the location feature, environmental and climatic factors significantly contributed to the spatial variability in HAZ.Conclusion: Many environmental, climatological, and socio-economic factors emerge as predictors for HAZ variability.It is essential to consider their complexity for comprehensive interventions targeting childhood stunting in Rwanda and similar settings.