Analyzing Male Domestic Violence through Exploratory Data Analysis and Explainable Machine Learning Insights

• URL: http://arxiv.org/abs/2403.15594v1
• Date: Fri, 22 Mar 2024 19:53:21 GMT
• Title: Analyzing Male Domestic Violence through Exploratory Data Analysis and Explainable Machine Learning Insights
• Authors: Md Abrar Jahin, Saleh Akram Naife, Fatema Tuj Johora Lima, M. F. Mridha, Jungpil Shin,
• Abstract summary: Existing literature predominantly emphasizes female victimization in domestic violence scenarios, leading to an absence of research on male victims. Our study represents a pioneering exploration of the underexplored realm of male domestic violence (MDV) within the Bangladeshi context. Our findings challenge the prevailing notion that domestic abuse primarily affects women, thus emphasizing the need for tailored interventions and support systems for male victims.
• Score: 0.5825410941577593
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Domestic violence, which is often perceived as a gendered issue among female victims, has gained increasing attention in recent years. Despite this focus, male victims of domestic abuse remain primarily overlooked, particularly in Bangladesh. Our study represents a pioneering exploration of the underexplored realm of male domestic violence (MDV) within the Bangladeshi context, shedding light on its prevalence, patterns, and underlying factors. Existing literature predominantly emphasizes female victimization in domestic violence scenarios, leading to an absence of research on male victims. We collected data from the major cities of Bangladesh and conducted exploratory data analysis to understand the underlying dynamics. We implemented 11 traditional machine learning models with default and optimized hyperparameters, 2 deep learning, and 4 ensemble models. Despite various approaches, CatBoost has emerged as the top performer due to its native support for categorical features, efficient handling of missing values, and robust regularization techniques, achieving 76% accuracy. In contrast, other models achieved accuracy rates in the range of 58-75%. The eXplainable AI techniques, SHAP and LIME, were employed to gain insights into the decision-making of black-box machine learning models. By shedding light on this topic and identifying factors associated with domestic abuse, the study contributes to identifying groups of people vulnerable to MDV, raising awareness, and informing policies and interventions aimed at reducing MDV. Our findings challenge the prevailing notion that domestic abuse primarily affects women, thus emphasizing the need for tailored interventions and support systems for male victims. ML techniques enhance the analysis and understanding of the data, providing valuable insights for developing effective strategies to combat this pressing social issue.

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