ArticlePublic health challenges2026
Comparison of Machine Learning Classifiers for Predicting Reported Antibiotic Treatment After Recent Fever/Cough Among Under-Five Children: A Pooled Analysis of DHS Data From 64 LMICs.
Article in Public health challenges, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Machine learning (ML) methods are increasingly used for health-related prediction. This study compared logistic regression with five ML algorithms for predicting reported antibiotic treatment following recent fever/cough among children under five using pooled Demographic and Health Survey (DHS) data. Methods: Data from 64 low- and middle-income countries (LMICs) were analyzed. The final analytic sample included 183,425 children with recent fever/cough, of whom 56,769 (30.95%) were reported to have received antibiotics. Logistic regression, Results: Overall predictive performance was modest. RF achieved the highest ROC-AUC (0.568, 95% CI: 0.552-0.591), whereas Conclusions: ML models did not demonstrate clear overall superiority over logistic regression. Although RF and
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