Investigation of Severe Injury Probability Prediction Models by Body Parts Through Decision Tree-Based Machine Learning Approach
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- 他サイトにて提供・販売
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- 文献番号
- 20254645
- 文献・情報種別
- International Journal of Automotive Engineering
Vol.16 No.4
- 掲載ページ
- 112-118(Total 7 p)
- 発行年月
- 2025年 10月
- 出版社
- (公社)自動車技術会
- 言語
- 英語
書誌事項
| カテゴリ(英) | Research paper 翻訳 |
|---|---|
| 著者(英) | 1) Yimeng Mei, 2) Haruto Fukushima, 3) Yusuke Miyazaki, 4) Fusako Sato |
| 勤務先(英) | 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 |
| 抄録(英) | 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. 翻訳 |