International Journal of Automotive Engineering
Online ISSN : 2185-0992
Print ISSN : 2185-0984
ISSN-L : 2185-0992
Research paper
Injury Probability Prediction Modeling Using Decision-Tree-Based Machine Learning Models
Tsubasa MiyazakiKeita TakahashiYusuke MiyazakiKoji KitamuraFusako Sato
著者情報
ジャーナル オープンアクセス

2024 年 15 巻 2 号 p. 66-73

詳細
抄録
Various machine learning models have been proposed to predict injuries to vehicle occupants from crash conditions, and optimized and evaluated for model accuracy using binary classification performance metrics. However, performance metrics for injury probability prediction have not been utilized to develop injury prediction models. Therefore, this paper developed injury probability prediction models using evaluation metrics to evaluate the probability prediction performance of injury prediction models and to verify the validity of injury probability predictions. The model constructed using a random forest performed better than the conventional injury prediction model constructed using logistic regression.
著者関連情報
© 2024 Society of Automotive Engineers of Japan, Inc

This article is licensed under a Creative Commons [Attribution-NonCommercial-ShareAlike 4.0 International] license.
https://creativecommons.org/licenses/by-nc-sa/4.0/
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