Getnet Bogale Begashaw

Child undernutrition remains a major public health challenge in Ethiopia, with stunting, underweight and wasting all highly prevalent among children under 5. Most existing Ethiopian studies have relied on cross-sectional designs and have analysed underweight, stunting and wasting as separate, independent outcomes, limiting insight into their longitudinal co-occurrence and shared determinants.

Abstract Child undernutrition remains a significant public-health issue in Ethiopia, shaped by socioeconomic, environmental, and health-related factors. This study develops a machine-learning (ML) classification model that accounts for concurrent nutritional outcomes in male and female children. Using baseline data from the Young Lives Cohort Study across five Ethiopian regions, we applied Random Forest (RF) for feature selection and trained multiple ML algorithms with k-fold cross-validation.