ArticleChildren (Basel, Switzerland)2022
Performance of Machine Learning Classifiers in Classifying Stunting among Under-Five Children in Zambia.
Article in Children (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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15 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
- Supervised machine learning-based prediction of modern contraceptive use among sexually active women in Nepal.PLOS digital health · 2026Article
- Predicting severe stunting and its determinants among under-five in Eastern African Countries: A machine learning algorithms.PloS one · 2026Article
- Machine learning-based prediction of fever among under-five children in Ethiopia: A national-level study.PloS one · 2026Article
- Using deep learning to monitor children's nutritional status for child stunting identification.Frontiers in pediatrics · 2026Article
- Data science and artificial intelligence for maternal, newborn and child health: scoping review and thematic analysis.BMC public health · 2025Article
- Supervised machine learning for classification and prediction of stunting among under-five Egyptian children.BMC pediatrics · 2025Article
- Predicting stunting status among under five children in ethiopia using ensemblemachine learning algorithms.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
- Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms.PloS one · 2025Article
- Employing machine learning techniques for prediction of micronutrient supplementation status during pregnancy in East African Countries.Scientific reports · 2024Article
- Prediction of incomplete immunization among under-five children in East Africa from recent demographic and health surveys: a machine learning approach.Scientific reports · 2024Article
- Employing supervised machine learning algorithms for classification and prediction of anemia among youth girls in Ethiopia.Scientific reports · 2024Article
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10 authors.
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Abstract
Stunting is a global public health issue. We sought to train and evaluate machine learning (ML) classification algorithms on the Zambia Demographic Health Survey (ZDHS) dataset to predict stunting among children under the age of five in Zambia. We applied Logistic regression (LR), Random Forest (RF), SV classification (SVC), XG Boost (XgB) and Naïve Bayes (NB) algorithms to predict the probability of stunting among children under five years of age, on the 2018 ZDHS dataset. We calibrated predicted probabilities and plotted the calibration curves to compare model performance. We computed accuracy, recall, precision and F1 for each machine learning algorithm. About 2327 (34.2%) children were stunted. Thirteen of fifty-eight features were selected for inclusion in the model using random forest. Calibrating the predicted probabilities improved the performance of machine learning algorithms when evaluated using calibration curves. RF was the most accurate algorithm, with an accuracy score of 79% in the testing and 61.6% in the training data while Naïve Bayesian was the worst performing algorithm for predicting stunting among children under five in Zambia using the 2018 ZDHS dataset. ML models aids quick diagnosis of stunting and the timely development of interventions aimed at preventing stunting.
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