Identifying influential factors distinguishing recidivists among offender patients with a diagnosis of schizophrenia via machine learning algorithms

Abstract

PurposeThere is a lack of research on predictors of criminal recidivism of offender patients diagnosed with schizophrenia. Methods653 potential predictor variables were anlyzed in a set of 344 offender patients with a diagnosis of schizophrenia (209 reconvicted) using machine learning algorithms. As a novel methodological approach, null hypothesis significance testing (NHST), backward selection, logistic regression, trees, support vector machines (SVM), and naive bayes were used for preselecting variables. Subsequently the variables identified as most influential were used for machine learning algorithm building and evaluation. ResultsThe two final models (with/without imputation) predicted criminal recidivism with an accuracy of 81.7 % and 70.6 % and a predictive power (area under the curve, AUC) of 0.89 and 0.76 based on the following predictors: prescription of amisulpride prior to reoffending, suspended sentencing to imprisonment, legal complaints filed by relatives/therapists/public authorities, recent legal issues, number of offences leading to forensic treatment, anxiety upon discharge, being single, violence toward care team and constant breaking of rules during treatment, illegal opioid use, middle east as place of birth, and time span since the last psychiatric inpatient treatment. ConclusionResults provide new insight on possible factors influencing persistent offending in a specific subgroup of patients with a schizophrenic spectrum disorder.