Machine Learning Approaches to Child Malnutrition Prediction in Bangladesh: A Dual-Round BDHS Analysis with Concept Drift Detection and Web-Based Screening

Bangladesh’s child-malnutrition profile presents a paradox: stunting declined by 7.7 percentage points between BDHS 2017-18 and 2022, whereas wasting increased by 3.2 percentage points. No previous ML study has explained this divergence while also developing a deployable screening tool. This paper presents NutriGuard BD, integrating BDHS data (n = 10,705 children under five), a community survey of 52 mothers, and five key-informant interviews with paediatricians. We benchmark seven classifiers-LR, RF, GB, XGBoost, LightGBM, CatBoost, and MLP-with a novel Composite Vulnerability Index (CVI) and a leakage-free pipeline (SMOTE on training folds only, Optuna Bayesian optimisation, Youden’s J threshold tuning). CatBoost achieves the best dietary quality AUC (0.636; CV-AUC 0.632); after threshold optimisation, LightGBM achieves clinical recall of 0.769 (stunting) and 0.714 (underweight). Critically, all six non-MLP classifiers collapse to near-random wasting prediction in 2022 (AUC 0.479-0.497; 2017-18 baseline AUC=0.935)-the first architecture-independent surveillance-level evidence of concept drift in BDHS modelling. SHAP identifies wealth index (v190) as the dominant predictor (mean |ϕ| = 0.377 Spearman ρ > 0.97 cross-architecture). Iron-course completion is only 11.5%; its sole significant predictor is iron receipt in pregnancy (p = 0.018, ϕ = 0.292). NutriGuard BD delivers these models as a bilingual (English/Bangla) Django web application for community health workers with zero patient-data persistence.