Predicting Acute Respiratory Infection Risk in Under–Five Children Using Machine Learning: Evidence from Bangladesh

Background Children’s immune systems are particularly vulnerable to infections. In developing countries, malnutrition, diarrheal diseases, and acute respiratory infections (ARI) remain leading causes of illness and mortality among children. This study applied multiple machine learning (ML) techniques to identify key risk factors associated with ARI symptoms in Bangladesh. Methods Secondary data from the Bangladesh Demographic and Health Survey (BDHS) 2022 were analyzed to assess ARI risk factors. Feature selection was conducted using the SHAP algorithm, and six ML classifiers were trained and evaluated with 10-fold cross-validation. Model performance was measured using accuracy, sensitivity, specificity, precision, F1-score, G-mean, and ROC AUC. Results Among the classifiers, the Decision Tree (DT) model achieved the highest performance across several metrics, with accuracy, sensitivity, specificity, precision, F1-score, and G-mean all at 0.81, and an ROC AUC of 0.88. Random Forest (RF) and AdaBoost (AdaB) also demonstrated strong performance, with RF showing an accuracy of 0.79 and ROC AUC of 0.89, and AdaB achieving an accuracy of 0.78 and ROC AUC of 0.86. Key predictive features included fever, age groups, geographic division, and area of residence. Conclusions The Decision Tree classifier outperformed other ML models in predicting ARI risk among children under five in Bangladesh, closely followed by Random Forest and AdaBoost. These findings highlight the potential of ML approaches to support targeted interventions by identifying critical risk factors. Government strategies should focus on early detection, improved treatment of fever, and enhanced household conditions to mitigate ARI risk in young children.