摘要
目的采用机器学习算法建立大学生自伤行为预测模型并探究大学生自伤行为的高风险因素。方法 2022年11—12月,采用便利抽样选取791名河北某高校在校大学生,以是否发生自伤行为作为结果变量,纳入基本人口学资料进行统计,并进行青少年自我伤害问卷、习得性无助量表、人际需求问卷中文版、青少年生活事件量表和童年创伤经历问卷评估,运用SPSS 26.0对预测变量进行统计分析,通过随机森林、支持向量机、逻辑回归三种机器学习模型预测大学生自伤行为,以模型的准确率、F1分数、敏感度、特异性以及AUC值评估模型性能,选出最优模型,利用最优模型分析大学生自伤行为的高风险因素。结果 (1)单因素分析结果显示,大学生自伤行为检出率为42.4%(335/791),男生的检出率显著高于女生(χ^(2)=14.139,P<0.05);家庭中低月收入(3 000~5 999元)的个体自伤行为检出率显著高于其他家庭月收入个体(P<0.05)。(2)随机森林、支持向量机和逻辑回归模型的准确率依次为85.53%,85.96%,68.86%,F1分数依次为0.853,0.864,0.676;敏感度依次为83.91%,89.04%,64.91%;AUC值依次为0.92,0.89和0.73。(3)基于预测效能较优的随机森林算法分析大学生自伤行为的高风险因素前10位的特征变量依次为情感虐待、归属受挫、无助感、人际关系因子、绝望感、情感忽视、学习压力因子、家庭月收入、累赘感知以及健康适应因子。结论随机森林相较于支持向量机和逻辑回归对于预测大学生自伤行为的效果更优;影响大学生自伤的因素主要来源于环境因素、个体因素和人际因素。
Objective A machine learning algorithm was used to develop a predictive model of self-injury among college students and to explore the high-risk factors for self-injury among college students.Methods From November to December 2022,a convenience sample of 791 college students from a university in Hebei Province was selected.Whether the self-injurious behavior occurred or not was regarded as an outcome variable.The basic demographics data were collected for statistical analysis.The adolescent self-harm questionnaire,the acquired helplessness scale,the Chinese version of the interpersonal needs questionnaire,the adolescent life events scale,and the childhood traumatic experiences questionnaire were used for assessment.The predictor variables were statistically analyzed by SPSS 26.0 software,and the performance of the model was evaluated by random forest,support vector machine and logistic regression so as to predict the self-injury behavior of college students.The model performance was evaluated by the accuracy,F1 score,sensitivity,specificity,and AUC value of the model,and the optimal model was selected.Finally,the optimal model was used to analyze the high-risk factors of college students'self-injury behaviors.Results(1)The results of one-way ANOVA showed that the detection rate of self-injury behavior among college students was 42.4%(335/791),and the detection rate of male students was significantly higher than that of female students(χ^(2)=14.139,P<0.05).Individuals with lower-middle monthly household income(RMB 3000-5999)had a significantly higher detection rate of self-injury behavior than those with other monthly household income(P<0.05).(2)The accuracy of random forest,support vector machine,and logistic regression models were 85.53%,85.96%,and 68.86%,F1 scores were 0.853,0.864,and 0.676,and sensitivities were 83.91%,89.04%,and 64.91%,respectively.The AUCs of support vector machine,logistic regression models and random forest were 0.89,0.73 and 0.92.(3)The top ten characteristic variables of high risk facto
作者
成楠
廖润超
张琳钰
刘艳丽
车佳郡
李晓敏
刘海宁
Cheng Nan;Liao Runchao;Zhang Linyu;Liu Yanli;Che Jiajun;Li Xiaomin;Liu Haining(Department of Psychology,Chengde Medical University,Chengde 067000,China;Department of Biomedical Engineering,Chengde Medical University,Chengde 067000,China)
出处
《中华行为医学与脑科学杂志》
CAS
CSCD
北大核心
2023年第9期787-793,共7页
Chinese Journal of Behavioral Medicine and Brain Science
基金
河北省教育科学"十四五"规划课题(2203222)
河北省高层次人才资助项目(B20221019)
承德医学院大学生创新创业训练计划项目(2023010)。
关键词
自伤行为
机器学习
随机森林
预测模型
大学生
Self-injury
Machine-learning
Random forest
Prediction model
College students
