Comparison of three machine learning models to predict suicidal ideation and depression among Chinese adolescents: A cross-sectional study

Abstract

BackgroundMachine 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. MethodsTen 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). ResultsThe 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. ConclusionsThis RF model may prove valuable for predicting suicidal ideation, depression, and non-suicidal self-injury among the general population of Chinese adolescents.