Applying machine learning to predict stunting in children under 5 years old based on water, sanitation and hygiene behaviors and infrastructure

The results demonstrate the efficacy of machine learning algorithms, especially extreme gradient boosting to potentially inform targeted WaSH interventions for reducing childhood stunting in resource-limited settings. However, these findings require external validation in other populations, and the complete-case analysis approach (excluding 35% of children with missing data) may limit generalizability to settings with less systematic data collection.