Combating Class Imbalance for Infant Mortality Risk Modeling: Resampling Strategies in Brazil's Unified Health System - Morsoleto, Ricardo; Silva, Vinícius A.; Caliari, Juliano de S.; Miranda, Simone Mara F.; Ferreira, Hiran Nonato M. (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2025: Anais do XIII Symposium on Knowledge Discovery, Mining and LearningDO - 10.5753/kdmile.2025.247778, 2025)
This study addresses the global public health challenge of infant mortality (IM) by developing predictive machine learning models using nationally representative data from Brazil’s DATASUS system. Leveraging birth (SINASC, 2018–2022) and death records (SIM, 2018–2023), the research rigorously preprocesses data, applies probabilistic entity matching, and enriches features via Principal Component Analysis (PCA). To counteract severe class imbalance (infant deaths vs. non-deaths), multiple sampling techniques are evaluated: undersampling (Random Undersampler, Edited Nearest Neighbours), oversampling (Random Oversampler, ADASYN), and hybrid methods (SMOTETomek, SMOTEENN), both with and without PCA. The XGBoost classifier is employed with default hyperparameters. Key findings indicate that Random Undersampler (RU) achieves the highest recall (0.8031), critical for identifying true IM cases, while Random Oversampling (ROS) yields the best precision (0.6451), minimizing false positives. Edited Nearest Neighbours (ENN) with PCA achieves the optimal F1-score (0.4488), balancing precision and recall. The study concludes that sampling strategy selection should align with clinical priorities: RU for maximizing detection, ROS for reliability, and ENN for balanced performance. PCA integration showed negligible impact on results.