Prediction of Infant Mortality in Brazil using Machine Learning and Entity Matching on Brazilian Unified Health System's Data - 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)
This study applies Machine Learning (ML) to predict infant mortality (IM) in Brazil by integrating two key DataSUS databases (SINASC - live births and SIM - mortality) using probabilistic Record Linkage (Entity Matching). Four supervised ML models were tested: Decision Tree (DT), Logistic Regression (LR), Naive Bayes (NB), and Extreme Gradient Boost (XGB). The analysis utilized demographic, obstetric, prenatal, and newborn variables. Despite achieving high overall accuracy (>90%), all models demonstrated low precision (<0.5) and F1-scores (max 0.44 for XGB) in identifying actual death cases. This poor performance in detecting the minority class (deaths, representing only 0.81% of records) highlights significant challenges posed by severe class imbalance, even after applying the SMOTE oversampling technique. XGBoost yielded the best, though still insufficient, results among the models. The study also revealed higher mortality ratios for Black infants, males, and those born in the North and Northeast regions. While reinforcing ML’s relevance for public health analysis, the results underscore the difficulty in reliably predicting rare events like IM with the current approach. The authors conclude that improvements in data balancing, alternative Entity Matching techniques, and exploring deep learning models are necessary future steps to develop a robust predictive tool for supporting IM reduction policies in Brazil.