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. 2025 Apr 3;20(4):e0321153.
doi: 10.1371/journal.pone.0321153. eCollection 2025.

Positive relationship between education level and risk perception and behavioral response: A machine learning approach

Zhipeng Wei  1   2   3 , Zhichun Zhang  1   2   3 , Liping Guo  1   2   3 , Wenjie Zhou  2   3   4 , Kehu Yang  1   2   3
Affiliations

Positive relationship between education level and risk perception and behavioral response: A machine learning approach

Zhipeng Wei et al. PLoS One. .

Abstract

This paper aims to examine the influence mechanism of education level as a key situational factor in the relationship between risk perception and behavioral response, encompassing both behavioral intention and preparatory behavior. Utilizing non-parametric estimation techniques in machine learning, particularly the Random Forest and XGBoost algorithms, this study develops predictive models to analyze the impact of 27 influencing factors on behavioral responses following risk perception. The findings indicate that, while the model's fit for preparatory behavior is 25.71% and its fit for behavioral intention is below 20%, the model effectively identifies key influencing factors. Further analysis employing SHAP values demonstrates that education level not only exerts a significant influence but also exhibits varying effects across different educational groups. Moreover, statistical testing corroborates the importance of education level in the relationship between risk perception and behavioral response, providing a robust scientific foundation for the development of risk management policies.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Feature Impact Summary Based on SHAP Values.
left: (a) Feature Importance Ranking; Right: (b) Influence of Features on Preparatory Behavior Effectiveness.
Fig 2
Fig 2. Feature Impact Summary Based on SHAP Values.
Left: (a) Feature Importance Ranking; Right: (b) Influence of Features on Behavioral Intention Effectiveness.
See this image and copyright information in PMC

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