Machine learning prediction of conduct problems in children using the longitudinal <scp>ABCD</scp> study

Using factors previously linked to conduct problems, we built machine learning models to identify predictors of conduct problems in children over a 3-year period. A small number of self-report features can be used to predict persistent conduct problems with 90% or greater specificity and sensitivity up to 3 years after initial assessment. This suggests that parent and child self-report data, along with machine learning, can identify children at risk for persistent conduct problems.