Longitudinal analysis of adolescents at high risk of depression: Prediction models

Abstract

Background: This study aimed to develop a machine-learning-based predictive model to identify adolescents at high risk of depression using longitudinal analysis to determine changes in risk factors over time.

Methods: This longitudinal study used 4 years of data from the Korea Child and Youth Panel Survey (2018-2021). The classification of high-risk depression was the outcome variable, with predictors categorized into general characteristics and personal, family, and school factors. The machine learning algorithms used in the analysis included logistic regression, support vector machine, decision tree, random forest, and extreme gradient boosting.

Results: Among the 1833 adolescents classified as having a low risk of depression during the initial survey year, 27.8 % were identified as being at a high risk of depression over the subsequent 3 years. The extreme gradient boosting algorithm yielded the best performance with an area under the curve of 0.9302. The key predictors identified included violent tendencies, self-esteem, sleep duration, gender, and coercive parenting style.

Conclusion: A machine-learning-based predictive model for identifying adolescents at high risk of depression was developed. These findings provide a foundation for early screening and the development of intervention programs and policies aimed at mitigating adolescent depression risk.