Machine learning identifies different related factors associated with depression and suicidal ideation in Chinese children and adolescents

Abstract

BackgroundDepression and suicidal ideation often co-occur in children and adolescents, yet they possess distinct characteristics. This study sought to identify the different related factors associated with depression and suicidal ideation. MethodsA nationwide cross-sectional survey collected data from Chinese children and adolescents aged 8 to 18 (N = 160,962; 48.91 % girls). The survey included inquiries about demographics, depression, suicidal ideation, anxiety, perceived stress, academic burnout, internet addiction, non-suicidal self-injury, bullying, and being bullied. Fifteen machine learning algorithms were conducted to identify the different related factors associated with depression and suicidal ideation. Additionally, we conducted external validation on an independent sample of 1,812,889 children and adolescents. ResultsOur findings revealed seven related factors linked to depression and six associated with suicidal ideation, with average accuracy rates of 86.86 % and 85.82 %, respectively. For depression, the most influential factors were anxiety, perceived stress, academic burnout, internet addiction, non-suicidal self-injury, experience of bullying, and age. Similarly, anxiety, non-suicidal self-injury, perceived stress, internet addiction, academic burnout, and age emerged as paramount factors for suicidal ideation. Moreover, these related factors showed notable variations in their predictive capacities for depression and suicidal ideation across different subgroups. ConclusionAnxiety emerged as the predominant shared factor for both depression and suicidal ideation, whereas the other related factors displayed distinct predictive patterns for each condition. These findings highlight the critical need for tailored strategies from public mental health service providers and policymakers to address the pressing concerns of depression and suicidal ideation.