Version 1
: Received: 4 October 2024 / Approved: 4 October 2024 / Online: 4 October 2024 (06:04:24 CEST)
How to cite:
Bernal-Monroy, E. R.; Castañeda-Monroy, E. D.; Renteria-Ramos, R. R.; Campaña-Bastidas, S. E.; Barrera, J.; Palacios-Yampuezan, T. M.; González Gustin, O. L.; Tobar-Torres, C. F.; Ceballos-Villada, Z. R. Detection of Patterns of Victimization and Risk of Gender Violence Through Machine Learning Algorithms. Preprints2024, 2024100314. https://doi.org/10.20944/preprints202410.0314.v1
Bernal-Monroy, E. R.; Castañeda-Monroy, E. D.; Renteria-Ramos, R. R.; Campaña-Bastidas, S. E.; Barrera, J.; Palacios-Yampuezan, T. M.; González Gustin, O. L.; Tobar-Torres, C. F.; Ceballos-Villada, Z. R. Detection of Patterns of Victimization and Risk of Gender Violence Through Machine Learning Algorithms. Preprints 2024, 2024100314. https://doi.org/10.20944/preprints202410.0314.v1
Bernal-Monroy, E. R.; Castañeda-Monroy, E. D.; Renteria-Ramos, R. R.; Campaña-Bastidas, S. E.; Barrera, J.; Palacios-Yampuezan, T. M.; González Gustin, O. L.; Tobar-Torres, C. F.; Ceballos-Villada, Z. R. Detection of Patterns of Victimization and Risk of Gender Violence Through Machine Learning Algorithms. Preprints2024, 2024100314. https://doi.org/10.20944/preprints202410.0314.v1
APA Style
Bernal-Monroy, E. R., Castañeda-Monroy, E. D., Renteria-Ramos, R. R., Campaña-Bastidas, S. E., Barrera, J., Palacios-Yampuezan, T. M., González Gustin, O. L., Tobar-Torres, C. F., & Ceballos-Villada, Z. R. (2024). Detection of Patterns of Victimization and Risk of Gender Violence Through Machine Learning Algorithms. Preprints. https://doi.org/10.20944/preprints202410.0314.v1
Chicago/Turabian Style
Bernal-Monroy, E. R., Carlos Fernando Tobar-Torres and Zeneida Rocio Ceballos-Villada. 2024 "Detection of Patterns of Victimization and Risk of Gender Violence Through Machine Learning Algorithms" Preprints. https://doi.org/10.20944/preprints202410.0314.v1
Abstract
This paper explores the application of machine learning techniques and statistical analysis to identify 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 forest, decision trees, and multiclass classification algorithms combined with fuzzy classification techniques to handle incomplete data. Implemented in Python and R, the models were statistically validated to ensure their reliability. Analyses based on health data revealed key patterns of victimization and risks associated with gender-based violence in the region. This study presents a data science model that uses a social determinants approach to assess the characteristics and patterns of violence against women in the Pacific region of Nariño. The research was conducted within the framework of the Orquídeas Program of the Colombian Ministry of Science, Technology, and Innovation.
Keywords
Data science; Machine learning; Pacific; San Andrés de Tumaco; Gender violence; Violence against women
Subject
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
