A Machine Learning Approach to Screening for Child Undernutrition in Rural Bangladesh

This paper builds separate stunting, underweight, and wasting classifiers for rural under-five children from BDHS 2022, correcting class imbalance strictly inside a leakage-free training pipeline (SMOTE and class weighting applied only after the train–test split) and using recall as the primary metric. A class-weighted logistic regression gives the best recall on all three indicators and detects roughly twice as many wasted and underweight children as the closest published model on the same survey. A high-accuracy random forest that identifies none of the wasted children is shown to demonstrate why recall must be prioritised over accuracy on this imbalanced data.