Citation

Jahin, Md Abrar and Naife, Saleh Akram and Lima, Fatema Tuj Johora and Mridha, M. F. and Hossen, Md. Jakir (2025) Predicting male domestic violence using explainable ensemble learning and exploratory data analysis. Discover Applied Sciences, 8 (1). ISSN 3004-9261

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Abstract

Domestic violence is commonly viewed as a gendered issue that primarily affects women, which tends to leave male victims largely overlooked. This study presents a novel, data-driven analysis of male domestic violence (MDV) in Bangladesh, highlighting the factors that influence it and addressing the challenges posed by a significant categorical imbalance of 5:1 and limited data availability. We collected data from nine major cities in Bangladesh and conducted exploratory data analysis (EDA) to understand the underlying dynamics. EDA revealed patterns such as the high prevalence of verbal abuse, the influence of financial dependency, and the role of familial and socio-economic factors in MDV. To predict and analyze MDV, we implemented 10 traditional machine learning (ML) models, three deep learning models, and two ensemble models, including stacking and hybrid approaches. We propose a stacking ensemble model with ANN and CatBoost as base classifiers and Logistic Regression as the meta-model, which demonstrated the best performance, achieving 95% accuracy, a 99.29% AUC, and balanced metrics across evaluation criteria. Model-specific feature importance analysis of the base classifiers identified key features influencing their decision-making. Model-agnostic explainable AI techniques, such as SHAP and LIME, provided both local and global insights into the decision-making processes of the proposed model, thereby increasing transparency and interpretability. Statistical validation using paired t-tests with 10- fold cross-validation and Bonferroni correction (α = 0.0036) confirmed the superior performance of our proposed model over alternatives. Our findings challenge the prevailing notion that domestic abuse primarily affects women, emphasizing the need for tailored interventions and support systems for male victims. Our proposed framework improves the analysis, laying the groundwork for developing effective strategies to address this critical issue

Item Type:	Article
Uncontrolled Keywords:	Machine learning, ensemble learning
Subjects:	Q Science > QA Mathematics > QA299.6-433 Analysis
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	11 Feb 2026 01:02
Last Modified:	11 Feb 2026 01:02
URII:	http://shdl.mmu.edu.my/id/eprint/15332

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