Detection of Victimization Patterns and Risk of Gender Violence Through Machine Learning Algorithms - Edna Rocio Bernal-Monroy; Erika Dajanna Castañeda-Monroy; Rafael Ricardo Rentería-Ramos; Sixto Enrique Campaña-Bastidas; Jessica Barrera; Tania Maribel Palacios-Yampuezan; Olga Lucía González Gustin; Carlos Fernando Tobar-Torres; Zeneida Rocio Ceballos-Villada (Informatics)
This paper explores the application of machine learning techniques and statistical analysis to identify the patterns of victimization and the risk of gender-based violence in San Andrés de Tumaco, Nariño, Colombia. Models were developed to classify women according to their vulnerability and risk of suffering various forms of violence, which were integrated into a decision-making tool for local authorities. The algorithms employed include K-means for clustering, artificial neural networks, random forests, decision trees, and multiclass classification algorithms combined with fuzzy classification techniques to handle the incomplete data. Implemented in Python and R, the models were statistically validated to ensure their reliability. Analysis based on health data revealed the key victimization patterns and risks associated with gender-based violence in the region. This study presents a data science model that uses a social determinant approach to assess the characteristics and patterns of violence against women in the Pacific region of Nariño. This research was conducted within the framework of the Orquídeas Program of the Colombian Ministry of Science, Technology, and Innovation.