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Background: Machine learning (ML) algorithms based on various clinicodemographic, psychometric, and biographic factors have been used to predict depression, suicidal ideation, and suicide attempt in adolescents, but there is still a need for more accurate and efficient models for screening the general adolescent population. In this study, we compared various ML methods to identify a model that most accurately predicts suicidal ideation and level of depression in a large cohort of school-aged adolescents., Methods: Ten psychological scale scores and 20 sociodemographic parameters were collected from 10,243 Chinese adolescents in the first or second year of middle school and high school. These variables were then included in a random forest (RF) model, support vector machine (SVM) model, and decision tree model for factor screening, dichotomous prediction of suicidal ideation (yes/no), and trichotomous prediction of depression (no depression, mild-moderate depression, or major depression)., Results: The RF model demonstrated greater accuracy for predicting suicidal ideation (mean accuracy (ACC) = 87.3 %, SD = 3.2 %, area under curve (AUC) = 92.4 %) and depressive status (ACC = 84.0 %, SD = 2.8 %, AUC = 90.1 %) than SVM and decision tree models. We have also used the RF model to predict adolescents with both depression and suicidal ideation with satisfactory results. Significant differences were found in several sociodemographic parameters and scale scores among classification groups and differences in six factors between sexes., Conclusions: This RF model may prove valuable for predicting suicidal ideation, depression, and non-suicidal self-injury among the general population of Chinese adolescents., Competing Interests: Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022. Published by Elsevier B.V.)

Suicide is the fourth leading cause of death among adolescents. Studies have shown that persistent suicidal ideation has a more important effect on suicidal behavior. The objective of this study was to identify predictors of persistent suicidal ideation. Data were collected from 4225 Chinese middle and high school students. These adolescents were assessed for suicidal ideation at baseline and the second year. We used multinomial logistic regression (n = 4171) for the predictive effect of these factors on persistent suicidal ideation. We controlled for gender, residence, clinical diagnosis, clinical diagnosis family, suicide planning, and suicide attempts. Depressive symptoms are essential in predicting persistent suicidal ideation (OR = 14.0; p < 0.001). Persistent suicidal ideation was predicted by sleep disorders, such as poor sleep quality (OR = 2.3; p = 0.008), difficulty falling asleep (OR = 2.4; p = 0.005), frequently midnight awakening (OR = 1.9; p = 0.044), and frequent nightmares (OR = 2.1; p = 0.040). There was a significant association between concern with persistent ideation and parental-peer alienation (OR for father, 1.9[ p = 0.024]; OR for mother, 3.1[ p < 0.001]; OR for peer, 2.3[ p = 0.003]). All measures are based on self-report rather than objective assessment or clinical diagnostic assessment. Persistent suicidal ideation had a more important role in influencing suicide planning and attempt. Interventions targeting sleep disorders and attention to attachments in the home and school are particularly important in preventing persistent suicidal ideation in adolescents. • Two-fifths of adolescents with persistent suicidal ideation have a suicide plan or attempted suicide. • Self-reported depressive symptoms remain a strong predictor of suicidal ideation. • Sleep disorders and parent-peer alienation were associated with increased odds of persistent suicidal ideation. [ABSTRACT FROM AUTHOR]