Predicting mental health problems in adolescence using machine learning techniques.

ABSTRACT:

Background

Predicting which children will go on to develop mental health symptoms as adolescents is critical for early intervention and preventing future, severe negative outcomes. Although many aspects of a child's life, personality, and symptoms have been flagged as indicators, there is currently no model created to screen the general population for the risk of developing mental health problems. Additionally, the advent of machine learning techniques represents an exciting way to potentially improve upon the standard prediction modelling technique, logistic regression. Therefore, we aimed to I.) develop a model that can predict mental health problems in mid-adolescence II.) investigate if machine learning techniques (random forest, support vector machines, neural network, and XGB

SUBMITTER: Tate AE 

PROVIDER: S-EPMC7135284 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature