Investigation of Severe Injury Probability Prediction Models by Body Parts Through Decision Tree-Based Machine Learning Approach
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- Publication code
- 20254645
- Paper/Info type
- International Journal of Automotive Engineering
Vol.16 No.4
- Pages
- 112-118(Total 7 p)
- Date of publication
- Oct 2025
- Publisher
- JSAE
- Language
- English
Detailed Information
| Category(E) | Research paper |
|---|---|
| Author(E) | 1) Yimeng Mei, 2) Haruto Fukushima, 3) Yusuke Miyazaki, 4) Fusako Sato |
| Affiliation(E) | 1) Institute of Science Tokyo, Department of Systems and Control Engineering, 2) Institute of Science Tokyo, Department of Systems and Control Engineering, 3) Institute of Science Tokyo, Department of Systems and Control Engineering, 4) Japan Automobile Research Institute |
| Abstract(E) | The quick and accurate prediction of occupant injuries in motor vehicle collisions helps emergency services respond more effectively and reduce casualties. Existing studies have mainly concentrated on predicting overall injury severity rather than examining injuries to specific body parts, which limits the precision of injury assessment and targeted emergency response. In this study, we developed a random forest-based model to predict injury severity in different body parts, including the head, face, neck, chest, abdomen, spine, and limbs. This enables emergency services to deliver precise and targeted responses after collisions. Furthermore, it facilitates a correlation analysis between various collision-contributing factors and body part-specific injuries. |
