ArticleSocial psychiatry and psychiatric epidemiology2024
Using random forest to identify correlates of depression symptoms among adolescents.
Article in Social psychiatry and psychiatric epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Development of a machine learning-based screening model for the risk of depression among the elderly in China.BMC geriatrics · 2026Article
- Depression Recognition Using Daily Wearable-Derived Physiological Data.Sensors (Basel, Switzerland) · 2025Article
- Machine learning models for predicting the risk of depressive symptoms in Chinese college students.Frontiers in psychiatry · 2025Article
- Development and validation of machine learning models for predicting STAS in stage I lung adenocarcinoma with part-solid and solid nodules: a two-center study.Frontiers in oncology · 2025Article
- Explaining factors influencing students' depression with a deep learning approach.Frontiers in psychology · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
purposeAdolescent depression is a significant public health concern, and studying its multifaceted factors using traditional methods possess challenges. This study employs random forest (RF) algorithms to determine factors predicting adolescent depression scores.
methodsThis study utilized self-reported survey data from 56,008 Canadian students (grades 7-12) attending 182 schools during the 2021/22 academic year. RF algorithms were applied to identify the correlates of (i) depression scores (CESD-R-10) and (ii) presence of clinically relevant depression (CESD-R-10 ≥ 10).
resultsRF achieved a 71% explained variance, accurately predicting depression scores within a 3.40 unit margin. The top 10 correlates identified by RF included other measures of mental health (anxiety symptoms, flourishing, emotional dysregulation), home life (excessive parental expectations, happy home life, ability to talk to family), school connectedness, sleep duration, and gender. In predicting clinically relevant depression, the algorithm showed 84% accuracy, 0.89 sensitivity, and 0.79 AUROC, aligning closely with the correlates identified for depression score.
conclusionThis study highlights RF's utility in identifying important correlates of adolescent depressive symptoms. RF's natural hierarchy offers an advantage over traditional methods. The findings underscore the importance and additional potential of sleep health promotion and school belonging initiatives in preventing adolescent depression.
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