ArticleBMC medical informatics and decision making2021
A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Application of machine learning algorithms for prediction of abortion and its determinants among women of reproductive-age in East African countries.Scientific reports · 2026Article
- Machine learning-enhanced modeling approach for optimally predicting household level food insecurity in Ethiopia during COVID-19.Scientific reports · 2026Article
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- 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
- 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
- Advancing Nutritional Status Classification With Hybrid Artificial Intelligence: A Novel Methodological Approach.Brain and behavior · 2025Article
- Social and economic predictors of under-five stunting in Mexico: a comprehensive approach through the XGB model.Journal of global health · 2025Article
- Ensemble learning to predict short birth interval among reproductive-age women in Ethiopia: evidence from EDHS 2016-2019.BMC pregnancy and childbirth · 2025Article
- A deep learning approach for classifying and predicting children's nutritional status in Ethiopia using LSTM-FC neural networks.BioData mining · 2025Article
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- RISE: a novel unified framework for feature relevance in malnutrition analytics integrating statistical and expert insights.Frontiers in public health · 2025Article
- Exploring machine learning classification for community based health insurance enrollment in Ethiopia.Frontiers in public health · 2025Article
- Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms.PloS one · 2025Article
- A computational model to analyze the impact of birth weight-nutritional status pair on disease development and disease recovery.Health information science and systems · 2024Article
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3 authors.
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Abstract
backgroundUndernutrition is the main cause of child death in developing countries. This paper aimed to explore the efficacy of machine learning (ML) approaches in predicting under-five undernutrition in Ethiopian administrative zones and to identify the most important predictors.
methodThe study employed ML techniques using retrospective cross-sectional survey data from Ethiopia, a national-representative data collected in the year (2000, 2005, 2011, and 2016). We explored six commonly used ML algorithms; Logistic regression, Least Absolute Shrinkage and Selection Operator (L-1 regularization logistic regression), L-2 regularization (Ridge), Elastic net, neural network, and random forest (RF). Sensitivity, specificity, accuracy, and area under the curve were used to evaluate the performance of those models.
resultsBased on different performance evaluations, the RF algorithm was selected as the best ML model. In the order of importance; urban-rural settlement, literacy rate of parents, and place of residence were the major determinants of disparities of nutritional status for under-five children among Ethiopian administrative zones.
conclusionOur results showed that the considered machine learning classification algorithms can effectively predict the under-five undernutrition status in Ethiopian administrative zones. Persistent under-five undernutrition status was found in the northern part of Ethiopia. The identification of such high-risk zones could provide useful information to decision-makers trying to reduce child undernutrition.
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