Paper Information | SBC BOOKS

Fill in your paper information

Language (*)

Title (*)

Keywords (*)

Abstract (*)

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 underreporting already present in the source data.

Pages (*)

File Link

English Information

Title

Keywords

Abstract

(*) To change the order drag the item to the new position.

Authors

#	Name
1	Rafaela Lucca(rafaelalucca@usp.br)
2	Rafaela Lucca(rafaelalucca@usp.br)
3	Roseli Romero(rafrance@icmc.usp.br)

(*) To change the order drag the item to the new position.

Reference

#	Reference
1	Bernardo, A. M. C. S., & Rodrigues, A. L. L. de S. M. (2024). Violência sexual contra criança e adolescente na Amazônia: um panorama no Arquipélago do Marajó. Fonte Segura, Fórum Brasileiro de Segurança Pública.
2	Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
3	Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1–3.
4	Coelho, M. de M. F., Oriá, M. O. B., Tamboril, A. C. R., Moreira, W. C., Oliveira, B. A. de, Martins, M. C., et al. (2025). Temporal evolution and projections of violence against women in Brazil: Scenarios from 2013 to 2033. Journal of Interpersonal Violence, 08862605251355969.
5	González-Prieto, Á., Brú, A., Nuño, J. C., & González-Álvarez, J. L. (2021). Machine learning for risk assessment in gender-based crime. arXiv preprint arXiv:2106.11847.
6	Harrell, F. E. (2001). Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis (Vol. 608). Springer.
7	Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30.
8	Kozhevnikova, S., Yukhnenko, D., Scola, G., & Fazel, S. (2025). Machine learning for violence prediction: A systematic review and critical appraisal. arXiv preprint arXiv:2511.23118.
9	Lima, G. C. C., Passos, C. M. de, Pinheiro, A. L. S., Ribeiro, Í. J. S., & Maia, E. G. (2025). Temporal trend and epidemiological profile of notifications of violence against women in Brazil: 2014–2023. Epidemiologia e Serviços de Saúde, 34, e20240475.
10	Souto, H., Mello, R., & Furtado, A. (2019). An acoustic scene classification approach involving domestic violence using machine learning. In Encontro Nacional de Inteligência Artificial e Computacional (ENIAC) (pp. 705–716). SBC.
11	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288.
12	Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301–320.

Paper Registration

Fill in your paper information

English Information

Authors

Reference