| Liu Huan,Shi Peifang,Zhang Kun,Kang Li,Zhang Yan,Na Long,Wang Binhong,He Meiqing,Predictive modle for violence risk in hospitalized schizophrenia patients based on support vector machine[J].SICHUAN MENTAL HEALTH,2026,39(1):27-35 |
| Predictive modle for violence risk in hospitalized schizophrenia patients based on support vector machine |
DOI：10.11886/scjsws20250707001 |
| English keywords:LASSO regression Schizophrenia Violent risk Prediction model |
| Fund projects:太原市卫生健康人才能力提升专项行动科研项目（项目名称：基于机器学习算法的精神科暴力行为早期预警模型研究，项目编号：Y2023006） |
| Author Name | Affiliation | Postcode | | Liu Huan | Taiyuan Mental Hospital, Taiyuan 030045, China | 030045 | | Shi Peifang | Taiyuan Mental Hospital, Taiyuan 030045, China | 030045 | | Zhang Kun | Taiyuan Mental Hospital, Taiyuan 030045, China | 030045 | | Kang Li | Taiyuan Mental Hospital, Taiyuan 030045, China | 030045 | | Zhang Yan | Taiyuan Mental Hospital, Taiyuan 030045, China | 030045 | | Na Long | Taiyuan Mental Hospital, Taiyuan 030045, China | 030045 | | Wang Binhong | Taiyuan Mental Hospital, Taiyuan 030045, China | 030045 | | He Meiqing* | Taiyuan Mental Hospital, Taiyuan 030045, China | 030045 |
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| Background The violent aggressive behaviors of patients with schizophrenia usually have the characteristics of suddenness， unpredictability， high severity， and great difficulty in prevention. Early identification and accurate assessment of their risk of violent aggression have significant clinical significance.Objective To construct a predictive model for the violence risk in hospitalized patients with schizophrenia， to identify the key factors influencing the occurrence of violent behavior in these patients， so as to provide references for clinical precise quantitative assessment and early intervention.Methods A total of 200 patients with schizophrenia who were hospitalized at Taiyuan Psychiatric Hospital from March 2022 to September 2024 and met the diagnostic criteria of the International Classification of Diseases， eleventh edition （ICD-11） were collected to form the modeling cohort. They were randomly divided into a training set （n=140） and a test set （n=60） at a ratio of 7∶3. Based on the least absolute shrinkage and selection operator （LASSO） regression algorithm， the feature variables were screened and dimension-reduced. The support vector machine （SVM） from machine learning was selected for model training and prediction. The discrimination efficacy of the model was evaluated by the area under the receiver operating characteristic （ROC） curve （AUC）， accuracy， precision， sensitivity， specificity， F1 value， and Brier value.Results LASSO regression screening identified 16 feature variables. Pearson correlation analysis revealed a positive correlation between prior violent behavior frequency and clinical psychiatric symptom scores （r=0.580， P<0.01）， a positive correlation between hospitalization compliance and current disease status （r=0.550， P=0.003）， and a positive correlation between educational level and family per capita monthly income （r=0.367， P<0.01）. The SVM model achieved an AUC of 0.853， accuracy of 0.800， precision of 0.810， sensitivity of 0.895， specificity of 0.636， F1 value of 0.850， and Brier value of 0.168.Conclusion The SVM model has a relatively high level of applicability and overall predictive performance in the assessment of violent risk in schizophrenia patients， which is helpful for the early identification of violent risks in such patients. ［Funded by Specialized Research Project for Enhancing the Competence of Health Professionals in Taiyuan City （number， Y2023006）］ |
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