Machine learning prediction of conduct problems in children using the longitudinal ABCD study.
Background: Children with conduct problems are at elevated risk for negative psychosocial, educational, and behavioral outcomes. Identifying at‐risk children can aid in providing timely intervention and prevention, ultimately improving their long‐term outcomes. There is a need to develop screening t...
| Published in: | Journal of Child Psychology & Psychiatry Vol. 67; no. 3; pp. 390 - 400 |
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| Main Authors: | , , , , |
| Format: | Article |
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Wiley-Blackwell
Mar2026
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=191429003&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 191429003 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00219630 JYY jtl: Journal of Child Psychology & Psychiatry issn: 00219630 maglogo: Y pubinfo: dt: Mar2026 vid: 67 iid: 3 pid: 480 pub: Wiley-Blackwell artinfo: ui: 191429003 10.1111/jcpp.70057 ppf: 390 ppct: 10 formats: tig: atl: Machine learning prediction of conduct problems in children using the longitudinal ABCD study. aug: au: Berluti, Kathryn Amormino, Paige Potter, Alexandra Wshah, Safwan Marsh, Abigail affil: Department of Psychology, Georgetown University, Washington DC,, USA Department of Psychological Science, University of Vermont, Burlington VT,, USA Department of Computer Science, University of Vermont, Burlington VT,, USA su: Elementary schools Behavior disorders in children Prediction models Receiver operating characteristic curves Descriptive statistics Child Behavior Checklist Research Machine learning Oppositional defiant disorder in children sug: subj: Elementary schools Behavior disorders in children Elementary and Secondary Schools Prediction models Receiver operating characteristic curves Descriptive statistics Child Behavior Checklist Research Machine learning Oppositional defiant disorder in children keyword: ABCD study Conduct disorder conduct problems machine learning ABCD study Conduct disorder conduct problems machine learning ab: Background: Children with conduct problems are at elevated risk for negative psychosocial, educational, and behavioral outcomes. Identifying at‐risk children can aid in providing timely intervention and prevention, ultimately improving their long‐term outcomes. There is a need to develop screening tools to better identify at‐risk children who may benefit from early intervention. Methods: Data were collected from the longitudinal Adolescent Brain Cognitive Development (ABCD) Study. Children completed a baseline visit at age 9–10, then returned annually for 3 years (n = 3,517). We used machine learning classifiers (logistic regression, Naïve Bayes, support vector machine, and random forest) to predict conduct problems (i.e., conduct disorder or oppositional defiant disorder) in children after 1, 2, and 3 years. Results: The best‐performing model (the random forest classifier) predicted children at risk for conduct problems with an accuracy of 90% or greater (AUC = 0.98 at 1 year, AUC = 0.97 at 2 years, AUC = 0.97 at 3 years). A random forest classifier simplified to include only 10 features was able to predict conduct problems nearly as well (AUC = 0.97 at 1 year, AUC = 0.96 at 2 years, AUC = 0.97 at 3 years). Conclusions: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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