ArticlePublic health nutrition2022
Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia.
Article in Public health nutrition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 3 of them syntheses that pooled it.
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39 citing papers in PubMed, 3 syntheses or guidelines pooled it, 55 citations in OpenAlex.
- Artificial Intelligence in the Management of Malnutrition in Cancer Patients: A Systematic Review.Advances in nutrition (Bethesda, Md.) · 2025Pooled 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
- Artificial Intelligence in Malnutrition: A Systematic Literature Review.Advances in nutrition (Bethesda, Md.) · 2024Pooled it
- Predicting facility delivery and its determinants among reproductive-age women in Ethiopia using machine learning algorithm: evidence from performance monitoring for action Ethiopia 2019 dataset.BMC pregnancy and childbirth · 2026Article
- Supervised machine learning algorithms for classifications of gender-based violence in Somalia: a comparison of oversampling techniques.Scientific reports · 2026Article
- Identification and Screening of Lactate-Related Genes as Molecular Markers for Early Diagnosis of Steroid-Induced Osteonecrosis of the Femoral Head.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Article
- Predicting the effects of temperature variability on nutritional status of children under five in Sub-Saharan Africa using machine learning.Scientific reports · 2026Article
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- Leveraging machine learning algorithm to predict minimum dietary diversity among children aged 6-23 months in Ethiopia.PLOS global public health · 2026Article
- Data science and artificial intelligence for maternal, newborn and child health: scoping review and thematic analysis.BMC public health · 2025Article
- Effects of environment and globalization on the double and triple burdens of infection symptoms among under-five children across low-middle income countries using machine learning algorithms.Infectious diseases of poverty · 2025Article
- Development and Validation of a Predictive Model for Individual Risk Prediction of Stunting in Ethiopia: A Predictive Modeling Study.Health science reports · 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
- Supervised machine learning for classification and prediction of stunting among under-five Egyptian children.BMC pediatrics · 2025Article
- Identifying determinants of malnutrition in under-five children in Bangladesh: insights from the BDHS-2022 cross-sectional study.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
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveChild undernutrition is a global public health problem with serious implications. In this study, we estimate predictive algorithms for the determinants of childhood stunting by using various machine learning (ML) algorithms.
designThis study draws on data from the Ethiopian Demographic and Health Survey of 2016. Five ML algorithms including eXtreme gradient boosting, k-nearest neighbours (k-NN), random forest, neural network and the generalised linear models were considered to predict the socio-demographic risk factors for undernutrition in Ethiopia.
settingHouseholds in Ethiopia.
participantsA total of 9471 children below 5 years of age participated in this study.
resultsThe descriptive results show substantial regional variations in child stunting, wasting and underweight in Ethiopia. Also, among the five ML algorithms, xgbTree algorithm shows a better prediction ability than the generalised linear mixed algorithm. The best predicting algorithm (xgbTree) shows diverse important predictors of undernutrition across the three outcomes which include time to water source, anaemia history, child age greater than 30 months, small birth size and maternal underweight, among others.
conclusionsThe xgbTree algorithm was a reasonably superior ML algorithm for predicting childhood undernutrition in Ethiopia compared to other ML algorithms considered in this study. The findings support improvement in access to water supply, food security and fertility regulation, among others, in the quest to considerably improve childhood nutrition in Ethiopia.
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