Forecasting Dengue Outbreaks with Machine Learning: A Case Study in Porto Alegre - Oliveira, Franklin Sales de; Santos, Dulcinéia Esteves; Bruhn, Fábio Raphael Pascoti; Bohm, Bianca Conrad; Santana, Brenda Salenave (Anais da Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul (ERAMIA-RS); 2025: Anais da I Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região SulDO - 10.5753/eramiars.2025.16737, 2025)
Dengue remains a critical global health concern intensified by climatic changes. Despite advances, few studies validate machine learning models in subtropical regions or handle data imbalance. This work compares Decision Tree, Random Forest, SVM, and XGBoost to predict dengue case acceleration in Porto Alegre, Brazil (2020–2024). Using time-lagged climatic features and Balanced Accuracy optimization, XGBoost achieved the best performance (0.67), identifying 10–15-day averages of dew point and humidity as key predictors. The results highlight the value of imbalance-aware modeling for reliable early warning systems in public health.