Machine learning based prediction of high school student mental health

Recent increases in the prevalence rates of anxiety, depression, and suicidal ideation, especially in student populations, present an urgent need to develop targeted diagnostic and treatment tools. Given the substantial evidence of variation in mental health symptomatology, efforts to develop prevention and intervention strategies may benefit from machine learning based investigations of individual and group variability in predictors of anxiety, depression, and suicidal ideation. The present study investigated the use of random forest classifiers (RFC) in predicting anxiety and depression screening scores and suicidal ideation in 9th grade students from a Massachusetts public school district (n = 274). Our highly accurate (80-95%) predictive analyses identified a strong inverse relationship between academic performance and depression and anxiety and substantial gender and race based variation in specific academic and symptom level predictors of anxiety, depression, and suicidal ideation. These findings illustrate the need for personalized clinically relevant data collection and analyses to inform mental health programming, preventative measures, and interventions.