Gender-Based Violence in Paraguay: Selected characteristics and predictive models
DOI:
https://doi.org/10.62544/ucomscientia.v3i1.47Keywords:
Gender-based violence, Machine learning, Predictive models, Public policies, ParaguayAbstract
This study aims to identify the characteristics influencing gender-based violence in Paraguay using machine learning algorithms. Utilizing data from the National Survey on the Situation of Women in Paraguay (ENSIMUP), several predictive models, including Random Forest and Logistic Regression, were applied to analyze factors associated with gender-based violence. The methodology involved rigorous data preprocessing and careful feature selection to enhance model accuracy. The results highlighted the importance of factors such as age, income level, and housing conditions in predicting gender-based violence. The Random Forest model demonstrated superior performance by balancing precision and discriminatory capability. This study underscores the usefulness of predictive models in shaping public policies, suggesting that their integration can significantly improve strategies for preventing and responding to gender-based violence. Continued research with advanced models and data balancing techniques is recommended to optimize risk factor identification and strengthen data-driven policies.
References
Becker, G. S. (1975). Human capital: A theoretical and empirical analysis, with special reference to education. Midway Reprint.
Beytía, P. (2017). La estructura interna de la pobreza multidimensional. En Los invisibles: Por qué la pobreza y la exclusión social dejaron de ser prioridad(pp. 71–88). Instituto de Estudios de la Sociedad.
Bradley, Andrew P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145–1159. https://doi.org/10.1016/s0031-3203(96)00142-2
Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system.Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794. https://doi.org/10.1145/2939672.2939785
Connell, R. W. (2005). Masculinities. University of California Press.
Escobar Manero, E.(2024). Salud y Género. Observatorio de Salud Estudio de Comunicación.Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.
González-Prieto, Á., Brú, A., Nuño, J. C., & González-Álvarez, J. L. (2023). Hybrid machine learning methods for risk assessment in gender-based crime.Knowledge-Based Systems,260, 110130. https://doi.org/10.1016/j.knosys.2022.110130
Goode, W. (1971). Force and Violence in the Family. Journal of marriage and the family, 33(4), 624. https://doi.org/10.2307/349435
Goutte, C., & Gaussier, E. (2005). A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. European Conference on Information Retrieval, 345–359.
Heise, L. L. (1998). Gender-based violence: An overview of the issues. Reproductive Health Matters, 6(11), 6–13.
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013).Applied logistic regression.Wiley. https://doi.org/10.1002/9781118548387
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection.https://www.ijcai.org/Proceedings/95-2/Papers/016.pdf
Marbn, S., Mariscal, G., & Segovi, J. (2009). A data mining & knowledge discovery process model. En J. Ponce & A. Karahoc (Eds.),Data Mining and Knowledge Discovery in Real Life Applications. I-Tech Education and Publishing. https://doi.org/10.5772/6438
McDougal, L., Dehingia, N., Bhan, N., Singh, A., McAuley, J., & Raj, A. (2021). Opening closed doors: Using machine learning to explore factors associated with marital sexual violence in a cross-sectional study from India.BMJ Open,11(12), e053603. https://doi.org/10.1136/bmjopen-2021-053603
Organización de las Naciones Unidas Mujeres. (2017). El empoderamiento económico de la mujer en el cambiante mundo del trabajo.https://www.unwomen.org/sites/default/files/Headquarters/Attachments/Sections/CSW/61/CSW-Conclusions-61-SP-WEB.pdf
Organización Mundial de la Salud. (2002). Informe mundial sobre la violencia y la salud: resumen.https://iris.who.int/bitstream/handle/10665/43431/9275324220_spa.pdf
Powers, D. M. W. (2020). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. https://doi.org/10.48550/ARXIV.2010.16061
Rodríguez-Rodríguez, I., Rodríguez, J.-V., Pardo-Quiles, D.-J., Heras-González, P., & Chatzigiannakis, I. (2020). Modeling and forecasting gender-based violence through machine learning techniques.Applied Sciences,10(22), 8244. https://doi.org/10.3390/app10228244
Sadownik, A.R. (2023). Bronfenbrenner: Ecology of human development in ecology of collaboration. En International Perspectives on Early Childhood Education and Development(pp. 83–95). Springer International Publishing.
Sasaki, M. (2007). Morphogenesis of Flux Structure. AA PublicationsTan, P.N., Steinbach, M., Karpatne, A., & Kumar, V. (2019). Introduction to Data Mining. 2da ed. Pearson Education.
Vyas, S., & Watts, C. (2009). How does economic empowerment affect women’s risk of intimate partner violence in low and middle income countries? A systematic review of published evidence.Journal of International Development,21(5), 577-602. https://doi.org/10.1002/jid.1500
World Health Organization. (2014). Informe sobre la situación mundial de la prevención de la violencia 2014. https://www.who.int/publications/i/item/9789241564793
Yuliawan, D., Hakim, D. B., Juanda, B., & Fauzi, A. (2022). Integrated rural socio-economic vulnerability analysis in Lampung Province.Jurnal Perspektif Pembiayaan dan Pembangunan Daerah,10(3), 175-188. https://doi.org/10.22437/ppd.v10i3.17591
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Copyright (c) 2025 Federico Javier Beck, Agustina Lucia Alfonso González
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