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| ■論文No. |
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| ■ページ数 |
12ページ |
■発行日
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2024/11/01 |
| ■タイトル |
Technical Analysis of Occupational Fatal Accidents in Malaysia Using Machine Learning Techniques |
| ■タイトル(英語) |
Technical Analysis of Occupational Fatal Accidents in Malaysia Using Machine Learning Techniques |
| ■著者名 |
Hanane Zermane (Industrial Engineering Department, Faculty of Technology, University of Batna 2), Abderrahim Zermane (Safety Engineering Interest Group, Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysi |
| ■著者名(英語) |
Hanane Zermane (Industrial Engineering Department, Faculty of Technology, University of Batna 2), Abderrahim Zermane (Safety Engineering Interest Group, Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia), Mohd Zahirasri Mohd Tohir (Safety Engineering Interest Group, Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia) |
| ■価格 |
会員 ¥550 一般 ¥770 |
| ■書籍種類 |
論文誌(論文単位) |
| ■グループ名 |
【D】産業応用部門（英文） |
| ■本誌 |
IEEJ Journal of Industry Applications Vol.13 No.6 （2024）
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| ■本誌掲載ページ |
711-722ページ |
| ■原稿種別 |
論文／英語 |
| ■電子版へのリンク |
https://www.jstage.jst.go.jp/article/ieejjia/13/6/13_24002974/_article/-char/ja/
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| ■キーワード |
risk management，neuro-linguistic programming，machine learning，mixed-method analysis，prevention management |
| ■要約(日本語) |
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| ■要約(英語) |
With the rapid economic growth of Malaysia, workplace accidents have increased drastically, according to the Department of Occupational Safety and Health (DOSH). This study aimed to determine the patterns in Malaysian workplace fatal accidents. A total of 505 fatal accident cases across 15 industries were analyzed in this study using both qualitative and quantitative methods. These fatality cases were identified and recorded by the DOSH from 2010 to 2020. The data were arranged and coded in Python and analyzed in terms of frequency analysis, Spearman's rank order correlation, eta squared, chi-square, and Cramer's V methods. Furthermore, neuro-linguistic programming was performed for word cloud and sentiment analyses. Finally, a light gradient-boosting machine learning model was used to further understand the causes of fatalities in Malaysia. The results showed that fatal falls from heights were the highest contributor to fatal accidents (32%, n = 161). Workers under contract were more vulnerable to fatal accidents in the construction industry (n = 324, 64%) than other workers. General workers were the most susceptible category to fatal accidents (60%, n = 302). The results from this study provide valuable insights into workplace fatal accident patterns and strategies for their prevention across industries. |
| ■版 型 |
Ａ４ |
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