Work

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

Year: 2026

Type: article

Abstract: 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.

Source: European Journal of Criminology

DOI: 10.1177/14773708251412637

Authors: Ba Linh Le, Lucy Trafford, Sabina Firtala, Babatunde Williams

Institutions: Ludwig-Maximilians-Universität München, University of Oxford

Language: English

Topic: Intimate Partner and Family Violence

Subfield: Health

Field: Social Sciences

Domain: Social Sciences

SDGs: Gender equality, Peace, justice, and strong institutions, Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,650