  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.
