Domestic violence in Nepal: Insights from machine learning-based prediction.

INTRODUCTION

Conducting surveys on domestic violence across diverse countries, particularly in lower-middle-income nations like Nepal, poses significant challenges in understanding and addressing the multifaceted dynamics involved in domestic violence research. However, integrating machine learning can help uncover patterns and predictive factors. Therefore, this study aimed to evaluate and compare machine-learning models to identify population-level risk patterns of domestic violence associated with male demographic characteristics using nationally representative data from Nepal.

METHODOLOGY: We utilized nationally representative data from the Nepal Demographic and Health Surveys (DHS) conducted in 2016 and 2022. A total of 7,813 observations were analyzed. The outcome variable captured whether women reported experiencing any form of physical or sexual violence. Data preprocessing and analysis were conducted using Stata and Python, with machine learning models implemented through the PyCaret framework. Multiple algorithms were evaluated based on performance metrics including accuracy, precision, recall, F1-score, and AUC.

RESULT: Significant demographic shifts were observed between 2016 and 2022, including an increase in husbands with only primary education (from 23.2% to 42.52%) and rising rates of alcohol consumption. Among all models tested, LDA achieved the highest accuracy (74.61%) and F1-score (0.6924), while CatBoost and AdaBoost also showed competitive performance.

CONCLUSION

This study demonstrates the potential of machine learning models in predicting DV risk using male demographic profiles. While acknowledging that findings derived from Nepal-specific data may not be directly generalizable to other sociocultural settings, the findings highlight critical socio-economic determinants such as education, wealth, and substance use and support the use of predictive modeling as a complementary tool for early identification and targeted intervention.