Longitudinal Prediction of Mental Health Outcomes in Vulnerable Youth using Machine Learning

Abstract Mental illnesses affect almost 15% of the world’s population, with half of the cases emerging before age 14. Improved methods for predicting mental distress among adolescents, particularly in vulnerable populations, are needed. This study utilized traditional machine learning techniques to predict mental health status at age 17. We assessed the correlates of mental health outcomes in a sample of 632 adolescents with general mental distress (i.e., total difficulties score of 17 or higher) at age 11, who participated in the UK Millennium Cohort Study. Predictors measured at ages 11 and 14 were included in the analysis. Mental health status at age 17 was best predicted using a Balanced Random Forest model (AUC 0.75). Explainability techniques enabled the identification of several critical factors, such as school environment, emotional distress, sleep patterns, patience, and social network at ages 11 or 14, which were able to differentiate participants with poor or good mental health outcomes at age 17. Individuals experiencing persistent mental distress between the ages 11 and 17 were most likely to suffer from unhappiness and academic struggles. Our results point to potentially modifiable factors associated with the progression of mental distress in adolescents at high risk. These factors could pave the way for improved early intervention and preventive strategies for vulnerable young people during adolescence.