Random forest model combined with LASSO regression to analyze the influencing factors of aggressive behavior among high school students

LI Yaxin; HU Qibi; WANG Xingyu; LIU Xin; CHANG Hongjuan

doi:10.16835/j.cnki.1000-9817.2025292

Volume 46 Issue 10

Oct. 2025

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LI Yaxin, HU Qibi, WANG Xingyu, LIU Xin, CHANG Hongjuan. Random forest model combined with LASSO regression to analyze the influencing factors of aggressive behavior among high school students[J]. CHINESE JOURNAL OF SCHOOL HEALTH, 2025, 46(10): 1437-1442. doi: 10.16835/j.cnki.1000-9817.2025292

Citation:

LI Yaxin, HU Qibi, WANG Xingyu, LIU Xin, CHANG Hongjuan. Random forest model combined with LASSO regression to analyze the influencing factors of aggressive behavior among high school students[J]. CHINESE JOURNAL OF SCHOOL HEALTH, 2025, 46(10): 1437-1442. doi: 10.16835/j.cnki.1000-9817.2025292

Citation:

LI Yaxin, HU Qibi, WANG Xingyu, LIU Xin, CHANG Hongjuan. Random forest model combined with LASSO regression to analyze the influencing factors of aggressive behavior among high school students[J]. CHINESE JOURNAL OF SCHOOL HEALTH, 2025, 46(10): 1437-1442. doi: 10.16835/j.cnki.1000-9817.2025292

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Random forest model combined with LASSO regression to analyze the influencing factors of aggressive behavior among high school students

doi: 10.16835/j.cnki.1000-9817.2025292

School of Medicine, Wuhan University of Science and Technology, Wuhan 430000, Hubei Province, China

Received Date: 2025-05-08
Rev Recd Date: 2025-08-02
Publish Date: 2025-10-25

Abstract

Abstract

Objective To analyze the influencing factors of aggressive behaviors in high school students, rank the importance of relevant variables with visual presentation, so as to provide new references for developing intervention strategies. Methods In December 2024, a convenience sampling method was used to select 5 330 students from 5 high schools in Wuhan for an online questionnaire survey. The survey included the Buss-Perry Aggression Questionnaire (BPAQ), Interaction Anxiety Scale (IAS), Emotion Regulation Ability Scale, Inventory of Parent and Peer Attachment (IPPA), and a self-designed general information questionnaire. Variables were screened using LASSO regression, followed by multivariate Logistic regression analysis. The Random Forest model was then employed to rank and visualize variable importance. Results Univariate analysis and LASSO regression results indicated that gender, emotion regulation capability, paternal attachment quality, maternal attachment quality, maternal parenting style, paternal parenting style, parenting consistency, monthly household income per capita, school climate, relationship with classmates, interaction anxiety, and academic ranking were all significant variables (all P < 0.01). Multivariate Logistic regression analysis identified the following as influencing factors of aggressive behaviors in high school students: emotional management ability (OR=0.38, 95%CI=0.34-0.43), gender (OR=0.83, 95%CI=0.75-0.93), mother attachment quality (OR=0.89, 95%CI=0.74-1.07), father attachment quality (OR=0.76, 95%CI=0.66-0.87), interaction anxiousness (OR=1.58, 95%CI=1.40-1.78), consistency of parenting styles of parents (OR=1.40, 95%CI=1.21-1.62), school atmosphere (OR=1.50, 95%CI=1.14-1.98), academic performance ranking (upper-middle: OR=0.76, 95%CI=0.61-0.93; middle: OR=0.70, 95%CI=0.57-0.86) were the influencing factors of aggressive behavior in high school students (all P < 0.05). The results of Random Forest showed that emotional management ability was the most important factor affecting aggressive behavior in high school students. Conclusion Emotion regulation capability is identified as the core predictor of adolescent aggressive behaviors, which can significantly reduce the incidence of aggressive behaviors through enhanced emotion management.
- Models, statistical,
- Regression analysis,
- Aggression,
- Students

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References(31)

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