ArticlePloS one2021
Sociodemographic risk factors of under-five stunting in Bangladesh: Assessing the role of interactions using a machine learning method.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning in Predicting Child Malnutrition: A Meta-Analysis of Demographic and Health Surveys Data.International journal of environmental research and public health · 2025Pooled it
- Prevalence of Childhood Stunting and Its Associated Factors in Bangladesh: Quantile Regression Modeling Approach.Journal of nutrition and metabolism · 2026Article
- Caracterización y factores asociados a la malnutrición en menores de 5 años en el municipio de Neiva, Colombia.Global health promotion · 2025Article
- Socioeconomic Patterning of Stunting and Overweight Among Iranian Children Aged 2-5 Years: A National Cross-Sectional Analysis.Nutrients · 2025Article
- Social and economic predictors of under-five stunting in Mexico: a comprehensive approach through the XGB model.Journal of global health · 2025Article
- Identifying determinants of under-5 mortality in Bangladesh: A machine learning approach with BDHS 2022 data.PloS one · 2025Article
- Analyzing and forecasting under-5 mortality trends in Bangladesh using machine learning techniques.PloS one · 2025Article
- Trends and factors contributing to changes in childhood stunting in Bangladesh from 2012 to 2019: A multivariate decomposition modelling.PLOS global public health · 2025Article
- Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review.Journal of medical Internet research · 2024Article
- Machine Learning Approach for Predicting the Impact of Food Insecurity on Nutrient Consumption and Malnutrition in Children Aged 6 Months to 5 Years.Children (Basel, Switzerland) · 2024Article
- Statistical modelling of determinants of child stunting using secondary data and Bayesian networks: a UKRI Global Challenges Research Fund (GCRF) Action Against Stunting Hub protocol paper.BMJ paediatrics open · 2024Article
- Feature selection and association rule learning identify risk factors of malnutrition among Ethiopian schoolchildren.Frontiers in epidemiology · 2023Article
- Childhood stunting and subsequent educational outcomes: a marginal structural model analysis from a South African longitudinal study.Public health nutrition · 2022Article
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3 authors.
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Abstract
This paper aims to demonstrate the importance of studying interactions among various sociodemographic risk factors of childhood stunting in Bangladesh with the help of an interpretable machine learning method. Data used for the analyses are extracted from the Bangladesh Demographic and Health Survey (BDHS) 2014 and pertain to a sample of 6,170 under-5 children. Social and economic determinants such as wealth, mother's decision making on healthcare, parental education are considered in addition to geographic divisions and common demographic characteristics of children including age, sex and birth order. A classification tree was first constructed to identify important interaction-based rules that characterize children with different profiles of risk for stunting. Then binary logistic regression models were fitted to measure the importance of these interactions along with the individual risk factors. Results revealed that, as individual factors, living in Sylhet division (OR: 1.57; CI: 1.26-1.96), being an urban resident (OR: 1.28; CI: 1.03-1.96) and having working mothers (OR: 1.21; CI: 1.02-1.44) were associated with higher likelihoods of childhood stunting, whereas belonging to the richest households (OR: 0.56; CI: 0.35-0.90), higher BMI of mothers (OR: 0.68 CI: 0.56-0.84) and mothers' involvement in decision making about children's healthcare with father (OR: 0.83, CI: 0.71-0.97) were linked to lower likelihoods of stunting. Importantly however, risk classifications defined by the interplay of multiple sociodemographic factors showed more extreme odds ratios (OR) of stunting than single factor ORs. For example, children aged 14 months or above who belong to poor wealth class, have lowly educated fathers and reside in either Dhaka, Barisal, Chittagong or Sylhet division are the most vulnerable to stunting (OR: 2.52, CI: 1.85-3.44). The findings endorse the need for tailored-intervention programs for children based on their distinct risk profiles and sociodemographic characteristics.
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