Predicting mental health problems in adolescence using machine learning techniques
171 Citations•2020•
Ashley Tate, Ryan C. McCabe, Henrik Larsson
Develop a model that can predict mental health problems in mid-adolescence and investigate if machine learning techniques will outperform logistic regression, which would not be suitable for clinical use.
Abstract
Ultimately, our top performing model would not be suitable for clinical use, however it lays important groundwork for future models seeking to predict general mental health outcomes. Future studies should make use of parent-rated assessments when possible. Additionally, it may not be necessary for similar studies to forgo logistic regression in favor of other more complex methods.