Romero Estrella, González-González Jaime, Álvarez-Voces María, Costa-Montenegro Enrique, Díaz-Vázquez Beatriz, Busto-Castiñeira Andrea, Villar Paula, López-Romero Laura
Department of Clinical Psychology and Psychobiology, Institute of Psychology (IPsiUS), University of Santiago de Compostela, Campus Vida, Santiago de Compostela, Spain.
atlanTTic, Information Technologies Group, Universidade de Vigo, Vigo, Spain.
Front Public Health. 2025 Jun 12;13:1526413. doi: 10.3389/fpubh.2025.1526413. eCollection 2025.
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.
行为问题是影响儿童和青少年心理健康的最复杂、最具损害性且最普遍的挑战之一。由于其多方面的性质，开发能够捕捉影响因素之间复杂相互作用的预测模型非常重要。这项纵向研究旨在：（1）评估随机森林模型对不同行为问题水平儿童进行分类的效用和有效性，（2）分析个体和家庭变量在预测高水平行为问题时的相互作用，以及（3）确定用于准确儿童分类的最相关因素或因素组合。样本取自ELISA研究，由1352名儿童组成，他们在1年的时间内接受了两次评估。随机森林的使用及其内在结构能够识别具有预测儿童行为问题能力的变量子集。这项研究证明了将心理学见解与先进计算技术相结合以解决儿童心理健康关键问题的有效性，强调了加强筛查和量身定制干预措施的必要性。
