Tekstil Sektöründe İş Kazalarına Neden Olan Risk Faktörlerinin Veri Madenciliği Yöntemleriyle Değerlendirilmesi

Year 2024, Issue: 53, 84 - 96, 15.02.2024

Büşra Tunçman , Tülin Gündüz , Duygu Yılmaz Eroğlu

Abstract

Bu çalışma, veri madenciliği yöntemlerinin tekstil sektöründe iş kazalarının önlenmesinde yardımcı olabileceğini önermektedir. Çalışma kapsamında, 2019-2021 yılları arasında tekstil sektöründe meydana gelen 89.963 iş kazası verisi incelenmiş ve veri ön işleme çalışması ile örneklem sayısı 11.710’ a düşürülmüştür. Kaza sonucu oluşan yaralanma türlerinin tahmin edilmesinde model seçme haritası referans alınarak SVM, Ekstra Ağaçlar, Rastgele Orman, Gradient Boosting ve XGBoost algoritmaları seçilmiştir. Modeller makro F-skor performans metriği kullanılarak karşılaştırılmıştır. Veri dengeleme ve parametre optimizasyonu yöntemleri ile modellerin tahmin performansı artış göstermiştir. XGBoost algoritması %70 tahmin başarısı ile diğer algoritmalara göre daha iyi performans göstermiştir. SVM algoritması (%69) ve Ekstra Ağaçlar (%68) algoritması, yüksek makro F-skor değerlerine ulaşarak veri setini doğru yorumlayan modeller arasında yer almıştır. Tahmin sonucuna en çok etki eden özelliklerin sırasıyla kaza sebebi, kaza anında kullanılan araç/metaryel, alt sektör bilgisi ve firma büyüklüğü olduğu görülmüştür.

Keywords

Tekstil Sektörü, İş Kazaları, İş Güvenliği, Veri Madenciliği, Veri Dengeleme ve Hiperparametre Optimizasyonu.

Evaluation of Risk Factors Causing Occupational Accidents in the Textile Sector Using Data Mining Methods

Abstract

This study suggests that data mining methods can be helpful in preventing occupational accidents in the textile industry. Within the scope of the study, 89.963 occupational accident data that occurred in the textile sector between the years 2019-2021 were examined and the number of samples was reduced to 11.710 with the data preprocessing study. In estimating accidental injury types, model selection map was taken as reference and SVM, Extra Trees, Random Forest, Gradient Boosting and XGBoost algorithms were chosen. Models were compared using the macro F-score performance metric. The estimation performance of models has increased with data balancing and parameter optimization methods. XGBoost algorithm performed better than other algorithms with 70% prediction success. The SVM (69%) and Extra Trees (68%) have been among the algorithms that correctly interpreted the data set by reaching high macro F-score values. It has been seen that the features that have the most effect on the estimation result are cause of accident, material agent, sub-sector, and company size, respectively.

Keywords

Textile Sector, Occupational Accidents, Work Safety, Data Mining, Data Balancing and Hyperparameter Optimization.

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