ArticlePloS one2021
Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach.
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 31 papers, 3 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 3 syntheses or guidelines pooled it, 79 citations in OpenAlex.
- 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
- Risk or associated factors of wasting among under-five children in Bangladesh: A systematic review.Asia Pacific journal of clinical nutrition · 2024Pooled it
- Artificial Intelligence in Malnutrition: A Systematic Literature Review.Advances in nutrition (Bethesda, Md.) · 2024Pooled it
- Spatial distribution and predictors of stunting, wasting, and underweight among under-five children in Bangladesh.BMC public health · 2026Article
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- Evaluating algorithmic fairness of machine learning models in predicting underweight, overweight, and adiposity across socioeconomic and caste groups in India: evidence from the longitudinal ageing study in India.PLOS digital health · 2025Article
- How big data analytics can strengthen large-scale food fortification and biofortification decision-making: A scoping review.Annals of the New York Academy of Sciences · 2025Article
- Socioeconomic Risk Factors Associated With Acute Malnutrition Severity Among Under-Five Children Based on a Machine Learning Approach: The Case of Rural Emergency Contexts in Niger and Mali.Maternal & child nutrition · 2025Article
- Identifying determinants of malnutrition in under-five children in Bangladesh: insights from the BDHS-2022 cross-sectional study.Scientific reports · 2025Article
- Prediction of malnutrition in kids by integrating ResNet-50-based deep learning technique using facial images.Scientific reports · 2025Article
- A deep learning approach for classifying and predicting children's nutritional status in Ethiopia using LSTM-FC neural networks.BioData mining · 2025Article
- Predicting home delivery and identifying its determinants among women aged 15-49 years in sub-Saharan African countries using a Demographic and Health Surveys 2016-2023: a machine learning algorithm.BMC public health · 2025Article
- Prevalence of undernutrition and its associated factors among orphans aged 6-59 months in Nekemte town, Ethiopia.PloS one · 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
- Risk factors associated with underweight in children aged one to two years: a longitudinal study.BMC public health · 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
- Assessing risk factors for malnutrition among women in Bangladesh and forecasting malnutrition using machine learning approaches.BMC nutrition · 2024Article
- Environmental Predictors of Undernutrition Among Under-5 Children at Dabat District Health Facilities, Northwest Ethiopia, 2023.Environmental health insights · 2024Article
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Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsMalnutrition is a major health issue among Bangladeshi under-five (U5) children. Children are malnourished if the calories and proteins they take through their diet are not sufficient for their growth and maintenance. The goal of the research was to use machine learning (ML) algorithms to detect the risk factors of malnutrition (stunted, wasted, and underweight) as well as their prediction.
methodsThis work utilized malnutrition data that was derived from Bangladesh Demographic and Health Survey which was conducted in 2014. The selected dataset consisted of 7079 children with 13 factors. The potential risks of malnutrition have been identified by logistic regression (LR). Moreover, 3 ML classifiers (support vector machine (SVM), random forest (RF), and LR) have been implemented for predicting malnutrition and the performance of these ML algorithms were assessed on the basis of accuracy.
resultsThe average prevalence of stunted, wasted, and underweight was 35.4%, 15.4%, and 32.8%, respectively. It was noted that LR identified five risk factors for stunting and underweight, as well as four factors for wasting. Results illustrated that RF can be accurately classified as stunted, wasted, and underweight children and obtained the highest accuracy of 88.3% for stunted, 87.7% for wasted, and 85.7% for underweight.
conclusionThis research focused on the identification and prediction of major risk factors for stunting, wasting, and underweight using ML algorithms which will aid policymakers in reducing malnutrition among Bangladesh's U5 children.
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