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Using machine learning-based systems to help predict disengagement from the legal proceedings by women victims of intimate partner violence in Spain

Intimate partner violence against women (IPVW) is a pressing social issue which poses a challenge in terms of prevention, legal action, and reporting the abuse once it has occurred. However, a significant number of female victims who file a complaint against their abuser and initiate legal proceedin...

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Autores principales: Escobar-Linero, Elena, García-Jiménez, María, Trigo-Sánchez, María Eva, Cala-Carrillo, María Jesús, Sevillano, José Luis, Domínguez-Morales, Manuel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10246857/
https://www.ncbi.nlm.nih.gov/pubmed/37285361
http://dx.doi.org/10.1371/journal.pone.0276032
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author Escobar-Linero, Elena
García-Jiménez, María
Trigo-Sánchez, María Eva
Cala-Carrillo, María Jesús
Sevillano, José Luis
Domínguez-Morales, Manuel
author_facet Escobar-Linero, Elena
García-Jiménez, María
Trigo-Sánchez, María Eva
Cala-Carrillo, María Jesús
Sevillano, José Luis
Domínguez-Morales, Manuel
author_sort Escobar-Linero, Elena
collection PubMed
description Intimate partner violence against women (IPVW) is a pressing social issue which poses a challenge in terms of prevention, legal action, and reporting the abuse once it has occurred. However, a significant number of female victims who file a complaint against their abuser and initiate legal proceedings, subsequently, withdraw charges for different reasons. Research in this field has been focusing on identifying the factors underlying women victims’ decision to disengage from the legal process to enable intervention before this occurs. Previous studies have applied statistical models to use input variables and make a prediction of withdrawal. However, none have used machine learning models to predict disengagement from legal proceedings in IPVW cases. This could represent a more accurate way of detecting these events. This study applied machine learning (ML) techniques to predict the decision of IPVW victims to withdraw from prosecution. Three different ML algorithms were optimized and tested with the original dataset to assess the performance of ML models against non-linear input data. Once the best models had been obtained, explainable artificial intelligence (xAI) techniques were applied to search for the most informative input features and reduce the original dataset to the most important variables. Finally, these results were compared to those obtained in the previous work that used statistical techniques, and the set of most informative parameters was combined with the variables of the previous study, showing that ML-based models had a better predictive accuracy in all cases and that by adding one new variable to the previous work’s predictive model, the accuracy to detect withdrawal improved by 7.5%.
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spelling pubmed-102468572023-06-08 Using machine learning-based systems to help predict disengagement from the legal proceedings by women victims of intimate partner violence in Spain Escobar-Linero, Elena García-Jiménez, María Trigo-Sánchez, María Eva Cala-Carrillo, María Jesús Sevillano, José Luis Domínguez-Morales, Manuel PLoS One Research Article Intimate partner violence against women (IPVW) is a pressing social issue which poses a challenge in terms of prevention, legal action, and reporting the abuse once it has occurred. However, a significant number of female victims who file a complaint against their abuser and initiate legal proceedings, subsequently, withdraw charges for different reasons. Research in this field has been focusing on identifying the factors underlying women victims’ decision to disengage from the legal process to enable intervention before this occurs. Previous studies have applied statistical models to use input variables and make a prediction of withdrawal. However, none have used machine learning models to predict disengagement from legal proceedings in IPVW cases. This could represent a more accurate way of detecting these events. This study applied machine learning (ML) techniques to predict the decision of IPVW victims to withdraw from prosecution. Three different ML algorithms were optimized and tested with the original dataset to assess the performance of ML models against non-linear input data. Once the best models had been obtained, explainable artificial intelligence (xAI) techniques were applied to search for the most informative input features and reduce the original dataset to the most important variables. Finally, these results were compared to those obtained in the previous work that used statistical techniques, and the set of most informative parameters was combined with the variables of the previous study, showing that ML-based models had a better predictive accuracy in all cases and that by adding one new variable to the previous work’s predictive model, the accuracy to detect withdrawal improved by 7.5%. Public Library of Science 2023-06-07 /pmc/articles/PMC10246857/ /pubmed/37285361 http://dx.doi.org/10.1371/journal.pone.0276032 Text en © 2023 Escobar-Linero et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Escobar-Linero, Elena
García-Jiménez, María
Trigo-Sánchez, María Eva
Cala-Carrillo, María Jesús
Sevillano, José Luis
Domínguez-Morales, Manuel
Using machine learning-based systems to help predict disengagement from the legal proceedings by women victims of intimate partner violence in Spain
title Using machine learning-based systems to help predict disengagement from the legal proceedings by women victims of intimate partner violence in Spain
title_full Using machine learning-based systems to help predict disengagement from the legal proceedings by women victims of intimate partner violence in Spain
title_fullStr Using machine learning-based systems to help predict disengagement from the legal proceedings by women victims of intimate partner violence in Spain
title_full_unstemmed Using machine learning-based systems to help predict disengagement from the legal proceedings by women victims of intimate partner violence in Spain
title_short Using machine learning-based systems to help predict disengagement from the legal proceedings by women victims of intimate partner violence in Spain
title_sort using machine learning-based systems to help predict disengagement from the legal proceedings by women victims of intimate partner violence in spain
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10246857/
https://www.ncbi.nlm.nih.gov/pubmed/37285361
http://dx.doi.org/10.1371/journal.pone.0276032
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