A multinomial logit model based approach to find patterns among occupational accidents in Turkish manufacturing systems Türk imalat sistemlerinde iş kazalarındaki örüntülerin çok durumlu logit model’e dayalı bir yaklaşımla belirlenmesi

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Mutlu N. G., Selim S., ALTUNTAŞ S.

Journal of the Faculty of Engineering and Architecture of Gazi University, cilt.39, sa.2, ss.1049-1066, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 39 Sayı: 2
  • Basım Tarihi: 2024
  • Doi Numarası: 10.17341/gazimmfd.1131524
  • Dergi Adı: Journal of the Faculty of Engineering and Architecture of Gazi University
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Art Source, Compendex, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.1049-1066
  • Anahtar Kelimeler: decision tree analysis, health, Manufacturing industry, multinomial logit model, occupational accidents, occupational safety
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

Turkish manufacturing industry is in the top three in terms of occupational accident frequency among sectors. Therefore, there is a need to determine accident cause-effect relationships in order to improve occupational safety and minimize the risks that cause occupational accidents in the manufacturing industry. An integreated data driven approach is proposed to find patterns among occupational accidents in Turkish manufacturing systems. The proposed approach uses multinomial logit model (MLM) and decision tree algorithms, namely C5.0, Classification and Regression Trees (C&RT), The quaternion estimation (QUEST), Chi-square automatic interaction detector (CHAID) ve Random Trees. In this study, 307,590 occupational accidents in the Turkish manufacturing industry between 2013 and 2019 are used. It is found that there is a statistically significant relationship among division, geographical location of the accident, year, deviation, hour day, gender and age for all accidents with injury, death and loss of limb according to the absence of disability. Additionally, division, geographical location of the accident and year are among the top five predictors based on decision tree algorithms.