ArticleScientific reports2023
The application of machine learning approaches to determine the predictors of anemia among under five children in Ethiopia.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 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
- Identification of Key Genes Related to SUMOylation and Potassium Channels and Their Mechanism of Influence on Pulmonary Arterial Hypertension.Biochemical genetics · 2026Article
- An ensemble machine learning approach for predicting anemia among under-five children in malaria-endemic sub-Saharan African countries.Infectious diseases of poverty · 2026Article
- Machine learning models for predicting childhood anemia in Mozambique: analysis from national survey data.BMC pediatrics · 2026Article
- Improved prediction of childhood anemia using hybrid ensemble learning and dual-level explainability.Journal of public health research · 2026Article
- The stagnation of child anaemia (6-23 months) in Tanzania from 2004 to 2022: a missed opportunity during the 'first 1000 days'.Journal of global health · 2026Article
- Integrative transcriptomic and single-cell analysis reveals mitochondrial-related gene biomarkers in heart failure with preserved ejection fraction.Scientific reports · 2025Article
- Predicting Childhood Anaemia in Nigeria: A Machine Learning Approach to Uncover Key Risk Factors.Public health challenges · 2025Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Pediatrics 4.0: the Transformative Impacts of the Latest Industrial Revolution on Pediatrics.Health care analysis : HCA : journal of health philosophy and policy · 2025Article
- Transcriptome associated with single-cell analysis reveal the role of S-palmitoylation in coronary artery disease.Scientific reports · 2025Article
- Optimizing machine learning models for predicting anemia among under-five children in Ethiopia: insights from Ethiopian demographic and health survey data.BMC pediatrics · 2025Article
- A deep learning approach for classifying and predicting children's nutritional status in Ethiopia using LSTM-FC neural networks.BioData mining · 2025Article
- Differential diagnosis of iron deficiency anemia from aplastic anemia using machine learning and explainable Artificial Intelligence utilizing blood attributes.Scientific reports · 2025Article
- Applied artificial intelligence for global child health: Addressing biases and barriers.PLOS digital health · 2024Review
- Revolutionizing anemia detection: integrative machine learning models and advanced attention mechanisms.Visual computing for industry, biomedicine, and art · 2024Article
- Employing supervised machine learning algorithms for classification and prediction of anemia among youth girls in Ethiopia.Scientific reports · 2024Article
- Employing advanced supervised machine learning approaches for predicting micronutrient intake status among children aged 6-23 months in Ethiopia.Frontiers in nutrition · 2024Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Health professionals need a strong prediction system to reach appropriate disease diagnosis, particularly for under-five child with health problems like anemia. Diagnosis and treatment delay can potentially lead to devastating disease complications resulting in childhood mortality. However, the application of machine learning techniques using a large data set provides scientifically sounded information to solve such palpable critical health and health-related problems. Therefore, this study aimed to determine the predictors of anemia among under-5 year's age children in Ethiopia using a machine learning approach. A cross-sectional study design was done using the Ethiopian Demographic and Health Survey 2016 data set. A two-stage stratified cluster sampling technique was employed to select the samples. The data analysis was conducted using Statistical Package for Social Sciences/SPSS version 25 and R-software. Data were derived from Ethiopian Demographic and Health Survey. Boruta algorism was applied to select the features and determine the predictors of anemia among under-5 years-old children in Ethiopia. The machine learning algorism showed that number of children, distance to health facilities, health insurance coverage, youngest child's stool disposal, residence, mothers' wealth index, type of cooking fuel, number of family members, mothers' educational status and receiving rotavirus vaccine were the top ten important predictors for anemia among under-five children. Machine-learning algorithm was applied to determine the predictors of anemia among under- 5 year's age children in Ethiopia. We have identified the determinant factors by conducting a feature importance analysis with the Boruta algorithm. The most significant predictors were number of children, distance to health facility, health insurance coverage, youngest child's stool disposal, residence, mothers' wealth index, and type of cooking fuel. Machine learning model plays a paramount role for policy and intervention strategies related to anemia prevention and control among under-five children.
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