The Prediction and Influential Factors of Violence in Male Schizophrenia Patients With Machine Learning Algorithms.

Tao Yu,Xiuyan Liu,Xulai Zhang,Chunyuan Xu,Chenchen Deng

doi:10.3389/fpsyt.2022.799899

Abstract

BackgroundEarly to identify male schizophrenia patients with violence is important for the performance of targeted measures and closer monitoring, but it is difficult to use conventional risk factors. This study is aimed to employ machine learning (ML) algorithms combined with routine data to predict violent behavior among male schizophrenia patients. Moreover, the identified best model might be utilized to calculate the probability of an individual committing violence.MethodWe enrolled a total of 397 male schizophrenia patients and randomly stratified them into the training set and the testing set, in a 7:3 ratio. We used eight ML algorithms to develop the predictive models. The main variables as input features selected by the least absolute shrinkage and selection operator (LASSO) and logistic regression (LR) were integrated into prediction models for violence among male schizophrenia patients. In the training set, 10 × 10-fold cross-validation was conducted to adjust the parameters. In the testing set, we evaluated and compared the predictive performance of eight ML algorithms in terms of area under the curve (AUC) for the receiver operating characteristic curve.ResultOur results showed the prevalence of violence among male schizophrenia patients was 36.8%. The LASSO and LR identified main risk factors for violent behavior in patients with schizophrenia integrated into the predictive models, including lower education level [0.556 (0.378–0.816)], having cigarette smoking [2.121 (1.191–3.779)], higher positive syndrome [1.016 (1.002–1.031)] and higher social disability screening schedule (SDSS) [1.081 (1.026–1.139)]. The Neural Net (nnet) with an AUC of 0.6673 (0.5599–0.7748) had better prediction ability than that of other algorithms.ConclusionML algorithms are useful in early identifying male schizophrenia patients with violence and helping clinicians take preventive measures.

Highlights

Patients with schizophrenia are more likely to exhibit violent behavior, compared with the general population
Result: Our results showed the prevalence of violence among male schizophrenia patients was 36.8%
machine learning (ML) algorithms are useful in early identifying male schizophrenia patients with violence and helping clinicians take preventive measures

Summary

Introduction

Patients with schizophrenia are more likely to exhibit violent behavior, compared with the general population. The available violence risk assessment tools rely on self-reported information, possess limited effective predictive power, and need mental health professionals’ administer [5–7]. The potential approach to identify an individual at risk of violence based on objective data is needed. Given only a minority of patients with schizophrenia possess violent tendencies, researchers have attempted to find factors that increase the risk of violent behaviors. There are many risk factors identified by conventional statistical methods (hypothesis testing) including abnormal brain cortical characteristics [8] substance use disorder [9] personality disorders [10, 11] and childhood victimization [12], but it is difficult to integrate these risk factors into a model to subsequently predict an individual’s probability of committing violent behavior. To identify male schizophrenia patients with violence is important for the performance of targeted measures and closer monitoring, but it is difficult to use conventional risk factors. The identified best model might be utilized to calculate the probability of an individual committing violence

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Results

Conclusion