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A Point-of-Care Method with Integrated Decision Support Tool to Estimate Anemia at Population Level
Published on: January 19, 2024
Explainable machine learning for predicting childhood anemia in Sub-Saharan Africa using population-based DHS Data
Andualem Enyew Gedefaw1, Amanuel Worku2, Abraham Keffale Mengistu3
1Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
PLOS Global Public Health
|August 12, 2026
Summary
Childhood anemia prediction in Sub-Saharan Africa shows machine learning models offer moderate performance. While statistically significant improvements over traditional methods were observed, low sensitivity necessitates complementary screening strategies for effective intervention.
Area of Science:
- Public Health
- Computational Epidemiology
- Pediatrics
Background:
- Childhood anemia is a critical public health issue in Sub-Saharan Africa, impacting child development and survival.
- Prevalence exceeds 60% across 26 countries, demanding scalable prediction tools for targeted interventions.
Purpose of the Study:
- To evaluate the performance of various machine learning models in predicting childhood anemia using Demographic and Health Survey (DHS) data.
- To identify key predictors of childhood anemia for improved public health strategies.
Main Methods:
- Pooled DHS data from 26 Sub-Saharan African countries (110,251 children aged 6-59 months) were analyzed.
- Eight machine learning models were trained and evaluated using hyperparameter tuning and 5-fold cross-validation.
- Model performance was assessed via accuracy, precision, recall, F1-score, ROC-AUC, with SHAP for interpretability.
Main Results:
- The CatBoost model achieved the highest performance (ROC-AUC = 0.84), outperforming logistic regression significantly (p < 0.001).
- Models showed moderate discriminatory ability with limited sensitivity (recall ≈ 0.33-0.41).
- Key predictors included residence type, height-for-age z-score, country, and child age.
Conclusions:
- Machine learning models offer moderate to strong predictive performance for childhood anemia using DHS data.
- Despite statistical significance, low sensitivity restricts their use as standalone screening tools.
- Findings emphasize early nutrition, undernutrition reduction, and integrated predictive analytics for resource allocation in high-burden regions.
