Leveraging Random Forests explainability for predictive modeling of children's conduct problems: insights from individual and family factors
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 longitudin...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1526413/full |
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| author | Estrella Romero Jaime González-González María Álvarez-Voces Enrique Costa-Montenegro Beatriz Díaz-Vázquez Andrea Busto-Castiñeira Paula Villar Laura López-Romero |
| author_facet | Estrella Romero Jaime González-González María Álvarez-Voces Enrique Costa-Montenegro Beatriz Díaz-Vázquez Andrea Busto-Castiñeira Paula Villar Laura López-Romero |
| author_sort | Estrella Romero |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c989db8b5f9d495a97d58dce090c7f3b |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-c989db8b5f9d495a97d58dce090c7f3b2025-08-20T03:25:45ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-06-011310.3389/fpubh.2025.15264131526413Leveraging Random Forests explainability for predictive modeling of children's conduct problems: insights from individual and family factorsEstrella Romero0Jaime González-González1María Álvarez-Voces2Enrique Costa-Montenegro3Beatriz Díaz-Vázquez4Andrea Busto-Castiñeira5Paula Villar6Laura López-Romero7Department of Clinical Psychology and Psychobiology, Institute of Psychology (IPsiUS), University of Santiago de Compostela, Campus Vida, Santiago de Compostela, SpainatlanTTic, Information Technologies Group, Universidade de Vigo, Vigo, SpainDepartment of Clinical Psychology and Psychobiology, Institute of Psychology (IPsiUS), University of Santiago de Compostela, Campus Vida, Santiago de Compostela, SpainatlanTTic, Information Technologies Group, Universidade de Vigo, Vigo, SpainDepartment of Clinical Psychology and Psychobiology, Institute of Psychology (IPsiUS), University of Santiago de Compostela, Campus Vida, Santiago de Compostela, SpainatlanTTic, Information Technologies Group, Universidade de Vigo, Vigo, SpainDepartment of Clinical Psychology and Psychobiology, Institute of Psychology (IPsiUS), University of Santiago de Compostela, Campus Vida, Santiago de Compostela, SpainDepartment of Clinical Psychology and Psychobiology, Institute of Psychology (IPsiUS), University of Santiago de Compostela, Campus Vida, Santiago de Compostela, SpainConduct 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.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1526413/fullconduct problemschildhoodRandom Forestfamily variablesindividual variablesexplainability |
| spellingShingle | Estrella Romero Jaime González-González María Álvarez-Voces Enrique Costa-Montenegro Beatriz Díaz-Vázquez Andrea Busto-Castiñeira Paula Villar Laura López-Romero Leveraging Random Forests explainability for predictive modeling of children's conduct problems: insights from individual and family factors Frontiers in Public Health conduct problems childhood Random Forest family variables individual variables explainability |
| title | Leveraging Random Forests explainability for predictive modeling of children's conduct problems: insights from individual and family factors |
| title_full | Leveraging Random Forests explainability for predictive modeling of children's conduct problems: insights from individual and family factors |
| title_fullStr | Leveraging Random Forests explainability for predictive modeling of children's conduct problems: insights from individual and family factors |
| title_full_unstemmed | Leveraging Random Forests explainability for predictive modeling of children's conduct problems: insights from individual and family factors |
| title_short | Leveraging Random Forests explainability for predictive modeling of children's conduct problems: insights from individual and family factors |
| title_sort | leveraging random forests explainability for predictive modeling of children s conduct problems insights from individual and family factors |
| topic | conduct problems childhood Random Forest family variables individual variables explainability |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1526413/full |
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