Machine Learning Applied to Improve Prevention of, Response to, and Understanding of Violence Against Women
Intimate partner violence (IPV) remains a critical issue that requires data-driven solutions to improve victim profiling and intervention strategies. This study introduces Mujer Segura, an innovative web application designed to collect structured data on IPV cases and predict their severity using ma...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Informatics |
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| Online Access: | https://www.mdpi.com/2227-9709/12/2/40 |
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| author | Mariana Carolyn Cruz-Mendoza Roberto Angel Melendez-Armenta Juana Canul-Reich Julio Muñoz-Benítez |
| author_facet | Mariana Carolyn Cruz-Mendoza Roberto Angel Melendez-Armenta Juana Canul-Reich Julio Muñoz-Benítez |
| author_sort | Mariana Carolyn Cruz-Mendoza |
| collection | DOAJ |
| description | Intimate partner violence (IPV) remains a critical issue that requires data-driven solutions to improve victim profiling and intervention strategies. This study introduces Mujer Segura, an innovative web application designed to collect structured data on IPV cases and predict their severity using machine learning models. The methodology integrates Random Forest (RF) and Gradient Boosting Classifier (GBC) algorithms to classify IPV cases by leveraging historical data for predictive analysis. The RF model achieved an accuracy of 97%, with a precision of 1.00 for non-severe cases and 0.96 for severe cases, recall values of 0.93 and 1.00 respectively, and an ROC AUC of 0.9534. The GBC model demonstrated an accuracy of 89%, with a precision of 1.00 for non-severe cases and 0.98 for severe cases, recall values of 0.95 and 1.00 respectively, and an ROC AUC of 0.9891. The application also integrates geospatial visualization tools to identify high-risk areas in the State of Mexico, enabling real-time interventions. These findings confirm that machine learning can enhance the timely detection of IPV cases and support evidence-based decision-making for public safety agencies. |
| format | Article |
| id | doaj-art-7582c2d3a554449f903bfeca0495ad5d |
| institution | OA Journals |
| issn | 2227-9709 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Informatics |
| spelling | doaj-art-7582c2d3a554449f903bfeca0495ad5d2025-08-20T02:21:10ZengMDPI AGInformatics2227-97092025-04-011224010.3390/informatics12020040Machine Learning Applied to Improve Prevention of, Response to, and Understanding of Violence Against WomenMariana Carolyn Cruz-Mendoza0Roberto Angel Melendez-Armenta1Juana Canul-Reich2Julio Muñoz-Benítez3División de Ingeniería en Sistemas Computacionales, Tecnológico Nacional de México-Tecnológico de Estudios Superiores de Valle de Bravo, km. 30 de la Carretera Monumento, Valle de Bravo 51200, MexicoAffective Computing and Educational Innovation Laboratory, Division of Graduate Studies and Research, Tecnológico Nacional de México-Instituto Tecnológico Superior de Misantla, Misantla 93821, MexicoDivisión Académica de Ciencias y Tecnologías de la Información, Universidad Juárez Autónoma de Tabasco, Av Universidad s/n, Magisterial, Villahermosa 86040, MexicoInstituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Luis Enrique Erro #1, Sta María Tonanzintla, San Andrés Cholula 72840, MexicoIntimate partner violence (IPV) remains a critical issue that requires data-driven solutions to improve victim profiling and intervention strategies. This study introduces Mujer Segura, an innovative web application designed to collect structured data on IPV cases and predict their severity using machine learning models. The methodology integrates Random Forest (RF) and Gradient Boosting Classifier (GBC) algorithms to classify IPV cases by leveraging historical data for predictive analysis. The RF model achieved an accuracy of 97%, with a precision of 1.00 for non-severe cases and 0.96 for severe cases, recall values of 0.93 and 1.00 respectively, and an ROC AUC of 0.9534. The GBC model demonstrated an accuracy of 89%, with a precision of 1.00 for non-severe cases and 0.98 for severe cases, recall values of 0.95 and 1.00 respectively, and an ROC AUC of 0.9891. The application also integrates geospatial visualization tools to identify high-risk areas in the State of Mexico, enabling real-time interventions. These findings confirm that machine learning can enhance the timely detection of IPV cases and support evidence-based decision-making for public safety agencies.https://www.mdpi.com/2227-9709/12/2/40gradient boosting classifiermachine learningprevention violencerandom forestweb platform |
| spellingShingle | Mariana Carolyn Cruz-Mendoza Roberto Angel Melendez-Armenta Juana Canul-Reich Julio Muñoz-Benítez Machine Learning Applied to Improve Prevention of, Response to, and Understanding of Violence Against Women Informatics gradient boosting classifier machine learning prevention violence random forest web platform |
| title | Machine Learning Applied to Improve Prevention of, Response to, and Understanding of Violence Against Women |
| title_full | Machine Learning Applied to Improve Prevention of, Response to, and Understanding of Violence Against Women |
| title_fullStr | Machine Learning Applied to Improve Prevention of, Response to, and Understanding of Violence Against Women |
| title_full_unstemmed | Machine Learning Applied to Improve Prevention of, Response to, and Understanding of Violence Against Women |
| title_short | Machine Learning Applied to Improve Prevention of, Response to, and Understanding of Violence Against Women |
| title_sort | machine learning applied to improve prevention of response to and understanding of violence against women |
| topic | gradient boosting classifier machine learning prevention violence random forest web platform |
| url | https://www.mdpi.com/2227-9709/12/2/40 |
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