ArticlePloS one2024
Application of machine learning methods for predicting childhood anaemia: Analysis of Ethiopian Demographic Health Survey of 2016.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- The application of machine learning for infant cries classification and pathological cries detection: A systematic review.Science progressPooled it
- 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
- 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
- Machine learning-based prediction of household sanitation facility access in Sub-Saharan Africa: insights from DHS data (2012-2024).Archives of public health = Archives belges de sante publique · 2025Article
- Early Childhood Anemia in Ghana: Prevalence and Predictors Using Machine Learning Techniques.Children (Basel, Switzerland) · 2025Article
- Predicting total healthcare demand using machine learning: separate and combined analysis of predisposing, enabling, and need factors.BMC health services research · 2025Article
- Predicting malnutrition-based anemia in geriatric patients using machine learning methods.Journal of evaluation in clinical practice · 2025Article
- Exploring Machine Learning Algorithms to Predict Diarrhea Disease and Identify its Determinants among Under-Five Years Children in East Africa.Journal of epidemiology and global health · 2024Article
- Assessment of non-fatal injuries among university students in Hainan: a machine learning approach to exploring key factors.Frontiers in public health · 2024Article
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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
Childhood anaemia is a public health problem in Ethiopia. Machine learning (ML) is a growing in medicine field to predict diseases. Diagnosis of childhood anaemia is resource intensive. The aim of this study is to apply machine learning (ML) algorithm to predict childhood anaemia using socio-demographic, economic, and maternal and child related variables. The study used data from 2016 Ethiopian demographic health survey (EDHS). We used Python software version 3.11 to apply and test ML algorithms through logistic regression, Random Forest (RF), Decision Tree, and K-Nearest Neighbours (KNN). We evaluated the performance of each of the ML algorithms using discrimination and calibration parameters. The predictive performance of the algorithms was between 60% and 66%. The logistic regression model was the best predictive model of ML with accuracy (66%), sensitivity (82%), specificity (42%), and AUC (69%), followed by RF with accuracy (64%), sensitivity (79%), specificity (42%), and AUC (63%). The logistic regression and the RF models of ML showed poorest family, child age category between 6 and 23 months, uneducated mother, unemployed mother, and stunting as high importance predictors of childhood anaemia. Applying logistic regression and RF models of ML can detect combinations of predictors of childhood anaemia that can be used in primary health care professionals.
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