The establishment of a random forest predictive model and analysis of influencing factors for psycho-logical crisis among adolescent
Objective To 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.Methods A total of 1 417 middle school students were surveyed using cluster sampling in two pha-ses,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 cri-teria 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 ana-lyzed.Results(1)The detection rate of moderate to severe depression was 10.02％(142/1 417),the de-tection 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 un-der 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).Conclusions The occurrence of adolescent psycho-logical crisis is closely related to symptom factors,protective factors and parental emotions,and has the signif-icance of predicting across time.The machine learning random forest algorithm can effectively identify psy-chological crisis individuals and identify sensitive crisis individual characteristics.
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