SirusPlos.xlsx.

Intimate partner violence (IPV) is a serious social problem in Chile. Understanding the patterns of internalization and the motivations maintaining it is crucial to design optimal treatments that ensure adherence and completeness. This, in addition, is essential to prevent revictimization and improve the quality of life of both victims and their children.The present study analyzes the success of a psychological treatment offered by a Chilean foundation helping IPV victims. A database analysis containing 1,279 cases was performed applying classical statistics and artificial intelligence methods. The aim of the research was to search for cluster grouping and to create a classification model that is able to predict IPV treatment completeness. The main results demonstrate the presence of two main clusters, one including victims who completed the treatment (cluster 1) and a second one containing victims who did not complete the treatment (cluster 2). Cluster classification using an XGBoost model of the treatment completeness had an accuracy of 81%. The results showed that living with the aggressor, age and educational level had the greatest impact on the classification. Considering these factors as input variables allow for a higher precision on the treatment completeness prediction. To our knowledge, this is the first study performed in Chile that uses AI for cluster grouping and for analyzing the variables contributing to the success of an IPV victims’ treatment.

亲密伴侣暴力（Intimate partner violence, IPV）是智利境内亟待解决的严峻社会问题。明晰暴力行为的内化模式与维持动因，对于设计可确保治疗依从性与完成度的最优干预方案至关重要；此外，这对于防止二次受害、提升受害者及其子女的生活质量亦不可或缺。本研究针对智利某帮扶亲密伴侣暴力受害者的基金会所提供的心理治疗项目的成效展开分析，采用经典统计学与人工智能方法，对包含1279个案例的数据库开展了分析。本研究的目标为开展聚类分组分析，并构建可预测亲密伴侣暴力治疗完成度的分类模型。核心结果显示存在两个主要聚类：聚类1为完成治疗的受害者群体，聚类2为未完成治疗的受害者群体。采用XGBoost模型开展治疗完成度聚类分类的准确率达81%。研究结果表明，与施暴者共同居住、年龄与受教育水平对分类结果的影响最为显著；将上述因素作为输入变量，可提升治疗完成度预测的精准度。据我们所知，本研究为智利境内首个运用人工智能开展聚类分组分析，并探究影响亲密伴侣暴力受害者治疗成效相关变量的研究。