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ORIGINAL RESEARCH article

Front. Public Health
Sec. Injury Prevention and Control
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1453650

Assessment of Non-fatal Injuries Among University Students in Hainan: A Machine Learning Approach to Exploring Key Factors

Provisionally accepted
Kang Lu Kang Lu Xiaodong Cao Xiaodong Cao *Lixia Wang Lixia Wang *Tao Huang Tao Huang *Lanfang Chen Lanfang Chen *Xiaodan Wang Xiaodan Wang *Qiao Li Qiao Li *
  • Hainan Medical University, Haikou, China

The final, formatted version of the article will be published soon.

    Injuries constitute a significant global public health concern, particularly among individuals aged 0-34. These injuries are affected by various social, psychological, and physiological factors and are no longer viewed merely as accidental occurrences.Existing research has identified multiple risk factors for injuries; however, they often focus on the cases of children or the elderly, neglecting the university students. Machine learning (ML) can provide advanced analytics and is better suited to complex, nonlinear data compared to traditional methods. That said, ML has been underutilized in injury research despite its great potential. To fill this gap, this study applies ML to analyze injury data among university students in Hainan Province. The purpose is to provide insights into developing effective prevention strategies. To explore the relationship between scores on the self-rating anxiety scale and self-rating depression scale and the risk of non-fatal injuries within one year, we categorized these scores into two groups using restricted cubic splines. Chi-square tests and LASSO regression analysis were employed to filter factors potentially associated with non-fatal injuries. The Synthetic Minority Over-Sampling Technique (SMOTE) was applied to balance the dataset. Subsequent analyses were conducted using random forest, logistic regression, decision tree, and XGBoost models. Each model underwent ten-fold cross-validation to mitigate overfitting, with hyperparameters being optimized to improve performance. SHAP was utilized to identify the primary factors influencing non-fatal injuries. The Random Forest model has proved effective in this study. It identified three primary risk factors for predicting non-fatal injuries: being male, favorable household financial situation, and stable relationship. Protective factors include reduced internet time and being an only child in the family. The study highlighted five key factors influencing non-fatal injuries: sex, household financial situation, relationship stability, internet time, and sibling status. In identifying these factors, the Random Forest, Logistic Regression, Decision Tree, and XGBoost models demonstrated varying effectiveness, with the Random Forest model exhibiting superior performance.

    Keywords: Non-fatal injuries, university students, machine learning, Hainan Province, Influencing factors

    Received: 23 Jun 2024; Accepted: 08 Nov 2024.

    Copyright: © 2024 Lu, Cao, Wang, Huang, Chen, Wang and Li. 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:
    Xiaodong Cao, Hainan Medical University, Haikou, China
    Lixia Wang, Hainan Medical University, Haikou, China
    Tao Huang, Hainan Medical University, Haikou, China
    Lanfang Chen, Hainan Medical University, Haikou, China
    Xiaodan Wang, Hainan Medical University, Haikou, China
    Qiao Li, Hainan Medical University, Haikou, China

    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.