Analyzing factors influencing the severity of occupational accidents in textile industry using decision tree algorithms

Mutlu, Nazli Gulum; Altuntaş, Serkan

doi:10.1007/s10586-022-03958-9

Cited by 3 publications

(1 citation statement)

References 63 publications

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“…Recently, numerous researchers [16,18,[22][23][24] applied machine learning and deep learning algorithms to unravel the cause-effect relationships of accidents, highlighting the influence of human factors, equipment malfunctions, and workplace conditions. Kim and Park [18] utilized multilinear regression to model the correlation between economic factors and accidents.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of the Severity and Cause and Effect of Occupational Accidents in South Korea

Tuganishuri,

Yum,

Adhikari

et al. 2023

Sustainability

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The industrial sector in South Korea has recently undergone significant growth; however, it is also known for its hazardous workplaces. Occupational accidents have had a widespread impact across various industries; therefore, the identification of accident-influencing factors is crucial to improve workplace safety. We analyzed the occupational accident database from the Ministry of Economy and Finance to examine the influencing factors, including worker information, project details, time-related variables, and accident descriptions. Exploratory and correspondence data analyses were performed to identify patterns and relationships between variables. We applied multinomial logistic models and random forest algorithms to understand the correlation between victim status and independent variables. Results showed that 67% of all accidents occurred among workers with less than one month of employment. The multinomial regression model achieved a prediction accuracy of 97.66% with a kappa value of 0.846, outperforming the random forest model (kappa = 0.844). The receiver operating curve illustrated that the random forest had higher misclassification rates when distinguishing between injuries and fatalities. To mitigate accidents among new workers, enhanced safety training and protective measures are needed to enforce a healthy workplace. This study contributes to ongoing efforts to advance workplace safety, reduce occupational accidents, and promote a healthier working environment.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of the Severity and Cause and Effect of Occupational Accidents in South Korea

Tuganishuri,

Yum,

Adhikari

et al. 2023

Sustainability

View full text Add to dashboard Cite

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Cleaner production evaluation system for textile industry: An empirical study from LCA perspectives

Xu,

Yu,

Zhang

et al. 2024

Science of The Total Environment

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A Study of Measurement Modeling of Decision Trees in Machine Learning Processes

Li,

Qin,

Wang

2024

Applied Mathematics and Nonlinear Sciences

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Accompanied by the rapid development of economy and science and technology, the ordinary measurement model with a single method of parameter determination and accuracy is not guaranteed, which has made it difficult to adapt to the measurement needs of complex data in industrial engineering and other systems. This study proposes a measurement model for complex data through the optimization of decision trees in the process of machine learning. Firstly, the gradient-boosting-based decision tree measurement model (GBDT) is constructed by analyzing the decision tree model, and then the model is solved. At the same time, latent variables were included in the model, SEM described the reflection relationship of explicit variables to latent variables, and the GBDT optimization model, including latent variables, was constructed by using the results of the model measurement, including latent variables. Then, for the measurement of multivariate data, the fusion convolutional network was used for image data feature extraction, and the combined measurement model with multi-source data fusion (MDF-DTFEE) was constructed on the basis of the decision tree measurement model. In the empirical analysis of the measurement model, the predicted and actual values of the model training were fitted between 4~60 mg/L and 5~45 ml/L, respectively, and its R² on the training set and test set were 0.948 and 0.886, respectively, with the RMSE lower than 1.2, and none of the MAPE exceeded 0.2. The practical application always had an error range of 1 mg/L, which is in line with the requirements. It fulfills the practical application requirements, demonstrates the practical value of the measurement model in this paper, and provides a useful solution for measuring complex data.

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Analyzing factors influencing the severity of occupational accidents in textile industry using decision tree algorithms

Cited by 3 publications

References 63 publications

Analysis of the Severity and Cause and Effect of Occupational Accidents in South Korea

Analysis of the Severity and Cause and Effect of Occupational Accidents in South Korea

Cleaner production evaluation system for textile industry: An empirical study from LCA perspectives

A Study of Measurement Modeling of Decision Trees in Machine Learning Processes

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