Hygiene and Preventive Medicine
The establishment of a random forest predictive model and analysis of influencing factors for psychological crisis among adolescent
Teng Shan, Wang Weijie, Gao Huan, Zhao Jiubo
Published 2024-07-20
Cite as Chin J Behav Med & Brain Sci, 2024, 33(7): 630-636. DOI: 10.3760/cma.j.cn371468-20231222-00316
Abstract
ObjectiveTo establish a predictive model of psychological crisis based on the machine learning random forest algorithm, and to analyze the influencing factors of psychological crisis among adole scent.
MethodsA total of 1 417 middle school students were surveyed using cluster sampling in two phases, in November 2020 and June 2021.Demographic data, symptom factors, protective factors were collected in the first investigation, and depression and suicide risk were measured in the second investigation. The criteria for psychological crisis were moderate to severe depression(depression score≥15) and high suicide risk(suicide risk score≥7) in the second measurement. SPSS 24.0 software was used for statistical analysis of variables, and the random forest machine learning predictive model for psychological crisis was established by using R version 4.1.1 software, and the high-estimating factors of adolescent psychological crisis were analyzed.
Results(1) The detection rate of moderate to severe depression was 10.02%(142/1 417), the detection rate of high suicide risk was 30.77%(436/1 417), and detection rate of the psychological crisis was 8.19%(116/1 417).(2) The sensitivity and specificity of psychological crisis prediction model were 0.79, 0.82, positive predictive value was 0.82, negative predictive value was 0.79, accuracy was 0.80 and area under curve was 0.88. (3) The top 10 characteristic variables of influencing factors of adolescent psychological crisis were depression, anxiety, suicidal ideation, self-harming behavior, cognitive flexibility-controllability, cognitive flexibility-selectivity, grit-persistence effort, grit-interest consistency, mother's mood and father's mood(model prediction accuracy was 0.023-0.163).
ConclusionsThe occurrence of adolescent psychological crisis is closely related to symptom factors, protective factors and parental emotions, and has the significance of predicting across time.The machine learning random forest algorithm can effectively identify psychological crisis individuals and identify sensitive crisis individual characteristics.
Key words:
Psychological crisis; Depression; Suicide risk; Machine learning; Prediction model; Adolescent
Contributor Information
Teng Shan
Mental Health Education and Counseling Center, Dongguan University of Technology, Dongguan 523808, China
Wang Weijie
Department of Psychology, School of Public Health, Southern Medical University, Guangzhou 510515, China
Gao Huan
School of Public Health, Sun Yat-sen University, Guangzhou 528478, China
Zhao Jiubo
Department of Psychology, School of Public Health, Southern Medical University, Guangzhou 510515, China
Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou 510260, China
