Andualem Enyew: Predicting Childhood Anemia in Sub-Saharan Africa
Andualem Enyew, Lecturer at University of Gondar, shared a post on LinkedIn about a recent article he and his colleagues co-authored, published in PLOS Global Public Health, adding:
”Predicting Childhood Anemia in Sub-Saharan Africa
A new study published in PLOS Global Public Health analyzed data from 110,251 children across 26 Sub-Saharan African countries (2016–2024) to evaluate how effectively machine learning can predict childhood anemia.
Key findings:
- High burden: Pooled childhood anemia prevalence exceeded 60% across the region, highlighting the magnitude of this public health challenge.
- CatBoost performed best: The CatBoost model achieved a ROC-AUC of 0.84, significantly outperforming traditional logistic regression (p less than 0.001).
- Key predictors: SHAP-based explainability identified residence type, height-for-age z-score, country, and child age among the most influential predictors.
The findings reinforce the importance of early childhood nutrition, particularly during the critical 6–23-month period, addressing chronic undernutrition, and developing context-specific strategies to reduce childhood anemia.
Predictive analytics could potentially support risk stratification, targeted interventions, and smarter allocation of limited health resources in high-burden settings but responsible implementation requires careful validation and integration with existing health systems.
Title: Explainable machine learning for predicting childhood anemia in Sub-Saharan Africa using population-based DHS Data (2016–2024)
Authors: Andualem Enyew Gedefaw, Amanuel Worku, Abraham Keffale Mengistu, Bewuketu Terefe, Eliyas Addisu Taye, Fentahun Bikale Kebede, Nebebe Demis Baykemagn, Tirualem Zeleke Yehuala, Jamilu Sani

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