Identification of violent patients with schizophrenia using a hybrid machine learning approach at the individual level

Abstract Despite numerous risk factors associated with violence in patients with schizophrenia, predicting and preventing violent behavior is still a challenge. At present, machine learning (ML) has become a promising strategy for guiding individualized assessment. To build an effective model to predict the risk of violence in patients with schizophrenia, we proposed a hybrid ML method to improve the prediction capability in 42 violent offenders with schizophrenia and 33 non-violent patients with schizophrenia. The results revealed that the final model, which combined multimodal data, achieved the highest prediction performance with an accuracy of 90.67%. Specifically, the model, which fused three modalities of neuroimaging data, achieved a better accuracy than other fused models. In addition, the msot discriminative neuroimaging features involved in the prefrontal-temporal cognitive circuit and striatum reward system, indicating that dysfunction in cortical-subcortical circuits might be associated with high risk of violence in patients with schizophrenia. This study provides the first evidence supporting that the combination of specific multimodal neuroimaging and clinical data in ML analysis can effectively identify violent patients with schizophrenia. Furthermore, this work is crucial for the development of neuro-prediction models that could facilitate individualized treatment and interventions for violent behaviors in patients with schizophrenia.