A Comparative Study of Machine Learning Algorithms for Predicting Domestic Violence Vulnerability in Liberian Women

Abstract

Abstract Domestic violence against women is a prevalent issue in Liberia, with nearly half of women reporting physical violence. However, research on the biosocial factors contributing to this issue remains limited. In this study, we aim to predict women's vulnerability to domestic violence using a machine learning approach, leveraging data from the Liberian Demographic and Health Survey (LDHS) conducted in 2019–2020. To achieve this goal, we employed seven different machine learning algorithms, including ANN, KNN, RF, DT, XGBoost, LightGBM, and CatBoost. Our analysis revealed that the LightGBM and RF models achieved the highest accuracy in predicting women's vulnerability to domestic violence in Liberia, with accuracy rates of 81% and 82%, respectively. One of the key features identified across multiple algorithms was the number of people who had experienced emotional violence. These findings offer important insights into the underlying characteristics and risk factors associated with domestic violence against women in Liberia. By utilizing machine learning techniques, we can better predict and understand this complex issue, ultimately contributing to the development of more effective prevention and intervention strategies.