Predictive Signatures of Violence Against Women in Brazilian Health Records: A Comparative Study Using Classifier Committees and Logistic Regression - Lucca, Rafaela M.; Romero, Roseli A. F. (Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais Estendidos do XLI Simpósio Brasileiro de Bancos de DadosDO - 10.5753/sbbd_estendido.2026.249686, 2026)
Violence against women is a serious public health problem in Brazil, yet predictive studies on its notified records remain scarce, and most existing work treats violence as a single phenomenon. This paper investigates whether the three main types of violence registered in the Brazilian Notifiable Diseases Information System (SINAN), namely physical, psychological, and sexual, have distinct predictive signatures. We work with 2.22 million notifications from women between 2018 and 2024 and compare a regularized logistic regression against two classifier committees, Random Forest and LightGBM, evaluated under a temporal train and test split. Our analysis prioritizes interpretability and calibration over raw performance. Results show that each type of violence has its own distinguishable signature, the non-linear gain over the linear model is marginal, and class balancing substantially degrades probability calibration in imbalanced outcomes. The model fails to detect roughly 44.9 percent of psychological violence cases, exposing how automated tools tend to mirror the under-reporting already present in the source data.