Establishment of suicide behavior prediction models for bipolar disorder patients using random forest and backpropagation neural network
10.3760/cma.j.cn371468-20240604-00261
- VernacularTitle:双相障碍患者自杀行为的随机森林算法和人工神经网络预测模型建立
- Author:
Yu XIE
1
;
Xiang ZHU
;
Yang YANG
;
Xialong CHENG
;
Binbin KAN
Author Information
1. 安徽师范大学教育科学学院，芜湖 241000
- Publication Type:Journal Article
- Keywords:
Bipolar disorder;
Suicide prediction;
Random forest;
Backpropagation neural network;
Machine learning
- From:
Chinese Journal of Behavioral Medicine and Brain Science
2024;33(12):1086-1092
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To predict suicidal behaviors in patients with bipolar disorder by constructing a machine learning model based on random forest and backpropagation neural network, and provide clinical decision support for the prevention and intervention of patient suicide.Methods:From January 2020 to August 2023, 1 005 patients with bipolar disorder were enrolled.The general clinical data and social dysfunction, anxiety, depression scores of all patients were collected.The random forest algorithm was applied for feature selection, and backpropagation neural network model was constructed for evaluating the model's fitting effect and predictive performance.Results:There were statistically significant differences in sociodemographic characteristics and physiological and psychological factors among the suicide attempt group ( n=293), suicide ideation group ( n=332) and non-suicidal group ( n=380) of patients with bipolar disorder( P<0.05).Using the random forest algorithm identified six main predictive variables: educational level, age, free triiodothyronine (FT3), cognitive impairment, hopelessness and psychogenic anxiety.The developed backpropagation neural network model achieved a precision rate of 79.3%, a recall rate of 79.6%, an F1 score of 79.4%, and an AUC of 0.89 on the test set, indicating that the model predictive performance has high accuracy and discriminative power. Conclusion:This study developed a machine learning model for predicting suicide in patients with bipolar disorder, which possesses high accuracy and discriminative ability, providing a decision-making basis for the prevention and intervention of suicidal behaviors in patients with bipolar disorder.
