Machine learning-based prediction of fever among under-five children in Ethiopia: A national-level study

Birhanu Betela Warssamo

PLOS ONE, 2026, vol. 21, issue 7, 1-20

Abstract: Fever remains a leading cause of morbidity and mortality among children under the age of five, particularly in low-resource settings like Ethiopia. Most existing studies in Ethiopia have relied predominantly on traditional statistical techniques, which may not fully capture complex patterns and nonlinear relationships within the data. To address this gap, the present study applies multiple machine learning algorithms to determine the most accurate model for predicting fever among under-five children in Ethiopia. This study utilized a total of 8,592 weighted child samples from the 2016 EDHS. After preprocessing, the dataset was randomly split into 70% training and 30% testing sets. Five machine learning algorithms; LR, RF, SVM, GNB, and DT were developed to predict fever. SMOTE was applied to address class imbalance in the training data. Model performance was assessed using accuracy, AUC, sensitivity, specificity, precision, F1-score, and balanced accuracy. Association rule mining via the Apriori algorithm was used to identify frequent patterns associated with fever. The overall prevalence of fever among under-five children was 14% (n = 1,197). Among the algorithms tested, the RF classifier achieved the best performance (accuracy: 92.2%, AUC: 0.958, sensitivity: 98.7%, specificity: 85.6%, F1-score: 0.8761). RF Gini importance and SHAP values ranked region, diarrhea & maternal factors as top predictors. Association rule mining revealed ten strong rules linking predictor variables with fever occurrence. The RF classifier demonstrated superior performance in predicting fever and identifying key features among under-five children. The results confirm that machine learning algorithms can serve as effective tools for accurately predicting childhood fever. These findings provide valuable insights for policymakers and public health stakeholders, supporting data-driven decision-making for targeted fever prevention strategies in Ethiopia.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0354265

DOI: 10.1371/journal.pone.0354265

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