Application of machine learning in predicting aggressive behaviors from hospitalized patients with schizophrenia - Cheng, Nuo; Guo, Meihao; Yan, Fang; Guo, Zhengjun; Meng, Jun; Ning, Kui; Zhang, Yanping; Duan, Zitian; Han, Yong; Wang, Changhong (Frontiers in Psychiatry, 2023)
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 of the occurrence of aggressive behaviors. Methods The cluster sampling method was used to select patients with schizophrenia who were hospitalized in our hospital from July 2019 to August 2021 as the survey objects, and they were divided into an aggressive behavior group (611 cases) and an non-aggressive behavior group (1426 cases) according to whether they experienced obvious aggressive behaviors during hospitalization. Self-administered General Condition Questionnaire, Insight and Treatment Attitude Questionnaire (ITAQ), Family APGAR (Adaptation, Partnership, Growth, Affection, Resolve) Questionnaire (APGAR), Social Support Rating Scale Questionnaire (SSRS) and Family Burden Scale of Disease Questionnaire (FBS) were used for the survey. The Multilayer Perceptron, Lasso, Support Vector Machine and Random Forest algorithms were used to build a predictive model for the occurrence of aggressive behaviors from hospitalized patients with schizophrenia and to evaluate its predictive effect. Nomogram was used to build a clinical application tool. Results The AUC values of the Multi-Layer Perceptron, Lasso, Support Vector Machine, and Random Forest were 0.898 (95% CI:0.871-0.920), 0.901 (95% CI:0.874-0.923), 0.900 (95% CI:0.874-0.923), and 0.953 (95% CI: 0.933-0.969), where the AUCs of the Random Forest and the remaining three models were statistically different (p < 0.0001), and the remaining three models were not statistically different in pair comparisons (p > 0.5). Conclusion Machine learning models can fairly predict aggressive behaviors in hospitalized patients with schizophrenia, among which Random Forest has the best predictive effect and has some value in clinical application.