ArticleJournal of preventive medicine and public health = Yebang Uihakhoe chi2023
Prediction of Stunting Among Under-5 Children in Rwanda Using Machine Learning Techniques.
Article in Journal of preventive medicine and public health = Yebang Uihakhoe chi, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 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
- Time-Series Analysis and Age-Stratified Forecasting of Diarrheal Disease in Rwanda Using SARIMA Models.Tropical medicine and infectious disease · 2026Article
- Using deep learning to monitor children's nutritional status for child stunting identification.Frontiers in pediatrics · 2026Article
- Predicting severe stunting and its determinants among under-five in Eastern African Countries: A machine learning algorithms.PloS one · 2026Article
- Systems biology insights into the molecular drivers of childhood stunting and implications for intervention.Frontiers in nutrition · 2026Review
- Data science and artificial intelligence for maternal, newborn and child health: scoping review and thematic analysis.BMC public health · 2025Article
- Exploring the multifactorial predictors of stunting in children under five: A systematic review of the literature, 2015-2024.Journal of public health research · 2025Review
- Development and Validation of a Predictive Model for Individual Risk Prediction of Stunting in Ethiopia: A Predictive Modeling Study.Health science reports · 2025Article
- Supervised machine learning for classification and prediction of stunting among under-five Egyptian children.BMC pediatrics · 2025Article
- Effect of single-parent versus dual-parent households on dietary intake and growth among under-five children in Rwanda: an analysis using directed acyclic graph.BMC nutrition · 2025Article
- The accuracy of a novel stunting risk detection application based on nutrition and sanitation indicators in children aged under five years.BMC nutrition · 2025Article
- Identification of amendable risk factors for childhood stunting at individual, household and community levels in Northern Province, Rwanda - a cross-sectional population-based study.BMC public health · 2025Article
- Social and economic predictors of under-five stunting in Mexico: a comprehensive approach through the XGB model.Journal of global health · 2025Article
- A deep learning approach for classifying and predicting children's nutritional status in Ethiopia using LSTM-FC neural networks.BioData mining · 2025Article
- RISE: a novel unified framework for feature relevance in malnutrition analytics integrating statistical and expert insights.Frontiers in public health · 2025Article
- Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms.PloS one · 2025Article
- Prevalence and associated risk factors of stunting too early: analysis of the 2020 Rwanda demographic and health survey.BMJ nutrition, prevention & health · 2025Article
- Forecasting acute childhood malnutrition in Kenya using machine learning and diverse sets of indicators.PloS one · 2025Article
- Development of a diagnostic predictive model for determining child stunting in Malawi: a comparative analysis of variable selection approaches.BMC medical research methodology · 2024Article
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4 authors.
Funding
Abstract
objectivesRwanda reported a stunting rate of 33% in 2020, decreasing from 38% in 2015; however, stunting remains an issue. Globally, child deaths from malnutrition stand at 45%. The best options for the early detection and treatment of stunting should be made a community policy priority, and health services remain an issue. Hence, this research aimed to develop a model for predicting stunting in Rwandan children.
methodsThe Rwanda Demographic and Health Survey 2019-2020 was used as secondary data. Stratified 10-fold cross-validation was used, and different machine learning classifiers were trained to predict stunting status. The prediction models were compared using different metrics, and the best model was chosen.
resultsThe best model was developed with the gradient boosting classifier algorithm, with a training accuracy of 80.49% based on the performance indicators of several models. Based on a confusion matrix, the test accuracy, sensitivity, specificity, and F1 were calculated, yielding the model's ability to classify stunting cases correctly at 79.33%, identify stunted children accurately at 72.51%, and categorize non-stunted children correctly at 94.49%, with an area under the curve of 0.89. The model found that the mother's height, television, the child's age, province, mother's education, birth weight, and childbirth size were the most important predictors of stunting status.
conclusionsTherefore, machine-learning techniques may be used in Rwanda to construct an accurate model that can detect the early stages of stunting and offer the best predictive attributes to help prevent and control stunting in under five Rwandan children.
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