Beyond physical violence: A machine learning framework for predicting IPV victimisation using multidimensional predictors

This research aims to develop a novel domestic abuse risk assessment tool, called Lizzy, for predicting the repeat victimisation of German female victims of physical intimate partner violence. Our approach includes actuarial and machine learning techniques based on data from a longitudinal online survey with a nationally representative sample of 3878 respondents (July to November 2023). Four algorithms were employed: CatBoost, XGBoost, Logistic Regression, and Random Forest. Logistic regression performed best with an accuracy of 0.82 and Area Under the Curve of 0.85. We find that predictors covering multiple dimensions of abuse, including physical abuse as well as economic, digital, and emotional abuse, contribute to model performance.