—Child malnutrition remains a major public-health
challenge in Bangladesh and is associated with increased mor
bidity, impaired physical growth, delayed cognitive development,
and elevated mortality risk among children under five years of
age. Conventional nutritional assessment relies on anthropomet
ric measurements and threshold-based interpretation, but these
approaches may not fully capture the combined influence of
child-level, maternal, household, socioeconomic, and geographic
factors. This paper presents a regression-based machine-learning
framework for predicting the weight-for-height Z-score (WHZ)
of Bangladeshi children aged 0–59 months using the Bangladesh
Demographic and Health Survey 2022 children recode dataset.
After applying inclusion and quality-control criteria, the final
analytic dataset contained 4,105 records and 24 selected at
tributes. The study compares Linear Regression, Decision Tree
Regressor, Random Forest Regressor, and K-Nearest Neighbors
Regressor using mean squared error, root mean squared error,
mean absolute error, and coefficient of determination. Feature
engineering was applied to derive child body mass index, weight
to-age ratio, height-to-age ratio, and age-group variables. In
addition, K-means clustering with K = 3 was used to explore
nutritional-risk grouping in an unsupervised manner. The Ran
dom Forest Regressor achieved the best validation performance
with R2 = 0.9339, RMSE = 0.3166, and MAE = 0.2128, and
maintained strong test performance with R2 = 0.9249, RMSE
=0.3558, and MAE = 0.2374. The results suggest that ensemble
regression combined with anthropometric feature engineering
can provide an effective data-driven approach for estimating
WHZ scores from survey variables, although causal inference
and deployment-level conclusions require further validation.
Index Terms—child malnutrition, WHZ score, machine learn
ing, Random Forest, regression, Bangladesh DHS, anthropomet
ric feature engineering, K-means clustering
