{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:09:11Z","timestamp":1760058551425,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T00:00:00Z","timestamp":1744329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/informatics12020040","type":"journal-article","created":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T10:05:51Z","timestamp":1744365951000},"page":"40","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Applied to Improve Prevention of, Response to, and Understanding of Violence Against Women"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3500-2500","authenticated-orcid":false,"given":"Mariana Carolyn","family":"Cruz-Mendoza","sequence":"first","affiliation":[{"name":"Divisi\u00f3n de Ingenier\u00eda en Sistemas Computacionales, Tecnol\u00f3gico Nacional de M\u00e9xico-Tecnol\u00f3gico de Estudios Superiores de Valle de Bravo, km. 30 de la Carretera Monumento, Valle de Bravo 51200, Mexico"},{"name":"Affective Computing and Educational Innovation Laboratory, Division of Graduate Studies and Research, Tecnol\u00f3gico Nacional de M\u00e9xico-Instituto Tecnol\u00f3gico Superior de Misantla, Misantla 93821, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8994-0944","authenticated-orcid":false,"given":"Roberto Angel","family":"Melendez-Armenta","sequence":"additional","affiliation":[{"name":"Affective Computing and Educational Innovation Laboratory, Division of Graduate Studies and Research, Tecnol\u00f3gico Nacional de M\u00e9xico-Instituto Tecnol\u00f3gico Superior de Misantla, Misantla 93821, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1893-1332","authenticated-orcid":false,"given":"Juana","family":"Canul-Reich","sequence":"additional","affiliation":[{"name":"Divisi\u00f3n Acad\u00e9mica de Ciencias y Tecnolog\u00edas de la Informaci\u00f3n, Universidad Ju\u00e1rez Aut\u00f3noma de Tabasco, Av Universidad s\/n, Magisterial, Villahermosa 86040, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7611-5029","authenticated-orcid":false,"given":"Julio","family":"Mu\u00f1oz-Ben\u00edtez","sequence":"additional","affiliation":[{"name":"Instituto Nacional de Astrof\u00edsica, \u00d3ptica y Electr\u00f3nica (INAOE), Luis Enrique Erro #1, Sta Mar\u00eda Tonanzintla, San Andr\u00e9s Cholula 72840, Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pokhriyal, D., Bahuguna, R., Memoria, M., and Kumar, R. 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