Assessment of non-fatal injuries among university students in Hainan: a machine learning approach to exploring key factors
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.
Authors
- Lu Kang (ORCID: https://orcid.org/0000-0001-8822-5818)
- Xiaodong Cao (ORCID: https://orcid.org/0000-0002-1531-5799)
- Tao Huang (ORCID: https://orcid.org/0000-0001-5897-4017)
- Qiao Li (ORCID: https://orcid.org/0000-0001-9709-7280)
- Xiaodan Wang (ORCID: https://orcid.org/0000-0002-1104-0345)
- Liping Chen (ORCID: https://orcid.org/0000-0002-5135-4007)
- Lixia Wang (ORCID: https://orcid.org/0000-0003-0893-1259)
Institutions
- Hainan Medical University (CN)
Publication Details
- Journal
- Frontiers in Public Health
- Published
- 2024-11-21
- DOI
- https://doi.org/10.3389/fpubh.2024.1453650
- Primary Topic
- Injury Epidemiology and Prevention
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China