Detection of Victimization Patterns and Risk of Gender Violence Through Machine Learning Algorithms
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...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Informatics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9709/12/1/21 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850279951900606464 |
|---|---|
| author | 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 |
| author_facet | 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 |
| author_sort | Edna Rocio Bernal-Monroy |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4b25a5ab0ffb42c8acd95dad64ee449a |
| institution | OA Journals |
| issn | 2227-9709 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Informatics |
| spelling | doaj-art-4b25a5ab0ffb42c8acd95dad64ee449a2025-08-20T01:48:56ZengMDPI AGInformatics2227-97092025-02-011212110.3390/informatics12010021Detection of Victimization Patterns and Risk of Gender Violence Through Machine Learning AlgorithmsEdna Rocio Bernal-Monroy0Erika Dajanna Castañeda-Monroy1Rafael Ricardo Rentería-Ramos2Sixto Enrique Campaña-Bastidas3Jessica Barrera4Tania Maribel Palacios-Yampuezan5Olga Lucía González Gustin6Carlos Fernando Tobar-Torres7Zeneida Rocio Ceballos-Villada8Department of Engineering, Technical Professional Institute, Faculty of Basic Sciences and Engineering, Universidad Nacional Abierta y a Distancial (UNAD), Calle 14 Sur # 14-23 Barrio Restrepo, Bogotá 111511, ColombiaDepartment of Engineering, Faculty of Electronic Engineering, Universidad Pedagógica y Tecnológica de Colombia (UPTC), Calle 4 A Sur # 15-134, Sogamoso 152211, ColombiaDepartment of Engineering, Technical Professional Institute, Faculty of Basic Sciences and Engineering, Universidad Nacional Abierta y a Distancial (UNAD), Calle 14 Sur # 14-23 Barrio Restrepo, Bogotá 111511, ColombiaDepartment of Engineering, Technical Professional Institute, Faculty of Basic Sciences and Engineering, Universidad Nacional Abierta y a Distancial (UNAD), Calle 14 Sur # 14-23 Barrio Restrepo, Bogotá 111511, ColombiaEngineering Department, Centro de Investigación y Desarrollo Tecnológico en Ciencias Aplicadas (CIDTCA), Cl. 11 #37-05, Pasto 520001, ColombiaDepartment of Engineering, Technical Professional Institute, Faculty of Basic Sciences and Engineering, Universidad Nacional Abierta y a Distancial (UNAD), Calle 14 Sur # 14-23 Barrio Restrepo, Bogotá 111511, ColombiaDepartment of Engineering, Technical Professional Institute, Faculty of Basic Sciences and Engineering, Universidad Nacional Abierta y a Distancial (UNAD), Calle 14 Sur # 14-23 Barrio Restrepo, Bogotá 111511, ColombiaDepartment of Engineering, Technical Professional Institute, Faculty of Basic Sciences and Engineering, Universidad Nacional Abierta y a Distancial (UNAD), Calle 14 Sur # 14-23 Barrio Restrepo, Bogotá 111511, ColombiaDepartment of Engineering, Technical Professional Institute, Faculty of Basic Sciences and Engineering, Universidad Nacional Abierta y a Distancial (UNAD), Calle 14 Sur # 14-23 Barrio Restrepo, Bogotá 111511, ColombiaThis 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.https://www.mdpi.com/2227-9709/12/1/21data sciencegender violencemachine learningPacificSan Andrés de Tumacoviolence against women |
| spellingShingle | 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 Detection of Victimization Patterns and Risk of Gender Violence Through Machine Learning Algorithms Informatics data science gender violence machine learning Pacific San Andrés de Tumaco violence against women |
| title | Detection of Victimization Patterns and Risk of Gender Violence Through Machine Learning Algorithms |
| title_full | Detection of Victimization Patterns and Risk of Gender Violence Through Machine Learning Algorithms |
| title_fullStr | Detection of Victimization Patterns and Risk of Gender Violence Through Machine Learning Algorithms |
| title_full_unstemmed | Detection of Victimization Patterns and Risk of Gender Violence Through Machine Learning Algorithms |
| title_short | Detection of Victimization Patterns and Risk of Gender Violence Through Machine Learning Algorithms |
| title_sort | detection of victimization patterns and risk of gender violence through machine learning algorithms |
| topic | data science gender violence machine learning Pacific San Andrés de Tumaco violence against women |
| url | https://www.mdpi.com/2227-9709/12/1/21 |
| work_keys_str_mv | AT ednarociobernalmonroy detectionofvictimizationpatternsandriskofgenderviolencethroughmachinelearningalgorithms AT erikadajannacastanedamonroy detectionofvictimizationpatternsandriskofgenderviolencethroughmachinelearningalgorithms AT rafaelricardorenteriaramos detectionofvictimizationpatternsandriskofgenderviolencethroughmachinelearningalgorithms AT sixtoenriquecampanabastidas detectionofvictimizationpatternsandriskofgenderviolencethroughmachinelearningalgorithms AT jessicabarrera detectionofvictimizationpatternsandriskofgenderviolencethroughmachinelearningalgorithms AT taniamaribelpalaciosyampuezan detectionofvictimizationpatternsandriskofgenderviolencethroughmachinelearningalgorithms AT olgaluciagonzalezgustin detectionofvictimizationpatternsandriskofgenderviolencethroughmachinelearningalgorithms AT carlosfernandotobartorres detectionofvictimizationpatternsandriskofgenderviolencethroughmachinelearningalgorithms AT zeneidarocioceballosvillada detectionofvictimizationpatternsandriskofgenderviolencethroughmachinelearningalgorithms |