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Machine learning prediction of conduct problems in children using the longitudinal ABCD study

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Longitudinal study predicts conduct problems with 90% accuracy in children, highlighting effective predictors.

Accuracy of the best model reached over 90%, suggesting effective early identification of conduct problems in children.
The random forest classifier showed AUC scores of 0.98 at 1 year and 0.97 at 2 and 3 years, indicating robust performance.
Using self-report features significantly simplifies predictions of conduct problems without compromising accuracy.
Machine learning tools, utilizing parental and child data, may dramatically enhance timely intervention strategies.

Berluti et al. (2025) studied this question.

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