Application of machine learning in predicting aggressive behaviors from hospitalized patients with schizophrenia

Objective: To establish a predictive model of aggressive behaviors from hospitalized patients with schizophrenia through applying multiple machine learning algorithms, to provide a reference for accurately predicting and preventing the occurrence of aggressive behaviors. Methods: Cluster sampling was used to select patients with schizophrenia hospitalized from July 2019 to August 2021, divided into an aggressive behavior group (611 cases) and a non-aggressive behavior group (1,426 cases) according to whether they experienced obvious aggressive behaviors during hospitalization. Surveys included a General Condition Questionnaire, Insight and Treatment Attitude Questionnaire (ITAQ), Family APGAR, Social Support Rating Scale (SSRS), and Family Burden Scale (FBS). Multi-Layer Perceptron (MLP), Lasso, Support Vector Machine (SVM), and Random Forest (RF) algorithms were used to build predictive models; performance was evaluated with ROC AUC. A nomogram was constructed for clinical application. Results: The AUCs were MLP 0.904 (95% CI: 0.877–0.926), Lasso 0.901 (95% CI: 0.874–0.923), SVM 0.902 (95% CI: 0.876–0.924), and RF 0.955 (95% CI: 0.935–0.970). RF had significantly higher AUC than the other three models (p < 0.0001); the other three did not differ (p > 0.5). Conclusion: Machine learning models can fairly predict aggressive behaviors in hospitalized patients with schizophrenia, with Random Forest showing the best predictive performance and clinical application value.

Nuo Cheng, Meihao Guo, Fang Yan, Zhengjun Guo, Jun Meng, Kui Ning, Yanping Zhang, Zitian Duan, Yong Han, Changhong Wang, Arjen Noordhof, Ioannis K Gallos, Maryam Ravan