Interpretable Machine Learning to Identify Risk Factors for Recidivism in Intimate Partner Violence

Intimate Partner Violence (IPV) remains a significant global health issue with severe consequences ranging from physical injury to death, with rates rising in recent years. Prediction of recidivism is critical for prevention and treatment. Using data from a four-year clinical study, we develop interpretable machine-learning models to identify features for physical assault recidivism among IPV offenders. To standardize clinician-assigned severity scores and address non-linear associations, we apply filtered target encoding, which reduces subjectivity and bias in assessment. We find that combining self-reported and partner-reported variables enhances predictive power. Through feature importance analyses, we identify factors associated with lower recidivism risk, including decreased substance use and avoiding partner contact, while separation processes correlate with higher reoffending likelihood. These findings advance IPV risk assessment by providing a deeper understanding of risk factors critical for improving treatment effectiveness and addressing disparities in IPV management.