Low-Cost Dengue Triage: Predicting Disease Severity using Machine Learning Without Laboratory Biomarkers - Rosa, Maximus B.; Tavares, Gabriel C.; Recamonde-Mendoza, Mariana (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais do XXVI Simpósio Brasileiro de Computação Aplicada à Saúde, 2026)
Dengue remains a major public health challenge in tropical regions, where early risk stratification is critical during outbreaks. We predict dengue severity using only demographic and clinical data available at patient triage, without laboratory tests or biomarkers. Using a nationwide Brazilian surveillance dataset with over 700,000 confirmed cases, we trained tree-based machine learning models to classify patients into the three World Health Organization severity categories: low risk, dengue with warning signs, and severe dengue. The best-performing model, a Random Forest enriched with urban hierarchy information, achieved a macro F1-score of 0.61 (± 0.001) in validation and 0.63 on an independent test set. SHAP analysis revealed clinically plausible predictors of severity, highlighting the potential of surveillance data to support early dengue triage in resource-constrained settings.