ORIGINAL RESEARCH article

Front. Public Health

Sec. Children and Health

Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1526413

This article is part of the Research TopicAdvances in Artificial Intelligence Applications that Support Psychosocial HealthView all 5 articles

Leveraging Random Forests explainability for predictive modeling of children's conduct problems: Insights from individual and family factors

Provisionally accepted
Estrella RomeroEstrella Romero1Jaime González-GonzálezJaime González-González2*María Álvarez-VocesMaría Álvarez-Voces1Enrique Costa-MontenegroEnrique Costa-Montenegro2Beatriz Díaz-VázquezBeatriz Díaz-Vázquez1Andrea Busto-CastiñeiraAndrea Busto-Castiñeira2Paula VillarPaula Villar1Laura López-RomeroLaura López-Romero1
  • 1Department of Clinical Psychology and Psychobiology, Institute of Psychology (IPsiUS), University of Santiago de Compostela, Campus Vida, Santiago de Compostela, Spain
  • 2atlanTTic Research Center, University of Vigo, Information Technologies Group, Telecommunication Engineering School, Vigo, Spain

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Conduct problems are among the most complex, impairing, and prevalent challenges affecting the mental health of children and adolescents. Due to their multifaceted nature, it is important to develop predictive models that capture the intricate interactions among contributing factors. This longitudinal study aims to: 1) evaluate the utility and effectiveness of Random Forest models for classifying children with varying levels of conduct problems, 2) analyze the interactions between individual and family variables in predicting high levels of conduct problems, and 3) determine the most relevant factors or combinations for accurate child classification. The sample was drawn from the ELISA study, and consisted of 1,352 children assessed twice within a 1-year frame.The use of Random Forest and its inherent structure allowed to identify subsets of variables with the capability of predicting Conduct Problems in children. This research demonstrates the effectiveness of integrating psychological insights with advanced computational techniques to address critical concerns in children's mental health, emphasizing the need for enhanced screening and tailored interventions.

Keywords: Conduct problems, childhood, random forest, Family variables, Individual variables, Explainability

Received: 07 Jan 2025; Accepted: 19 May 2025.

Copyright: © 2025 Romero, González-González, Álvarez-Voces, Costa-Montenegro, Díaz-Vázquez, Busto-Castiñeira, Villar and López-Romero. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Jaime González-González, atlanTTic Research Center, University of Vigo, Information Technologies Group, Telecommunication Engineering School, Vigo, Spain

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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