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Evaluating machine learning performance in predicting injury severity in agribusiness industries
ارزیابی عملکرد یادگیری ماشینی در پیش بینی شدت جراحات در صنایع کشاورزی-2019 Although machine learning methods have been used as an outcome prediction tool in many fields, their utilization
in predicting incident outcome in occupational safety is relatively new. This study tests the performance
of machine learning techniques in modeling and predicting occupational incidents severity with respect to accessible
information of injured workers in agribusiness industries using workers’ compensation claims. More
than 33,000 incidents within agribusiness industries in the Midwest of the United States for 2008–2016 were
analyzed. The total cost of incidents was extracted and classified from workers’ compensation claims. Supervised
machine learning algorithms for classification (support vector machines with linear, quadratic, and RBF kernels,
Boosted Trees, and Naïve Bayes) were applied. The models can predict injury severity classification based on
injured body part, body group, nature of injury, nature group, cause of injury, cause group, and age and tenure of
injured workers with the accuracy rate of 92–98%. The results emphasize the significance of quantitative
analysis of empirical injury data in safety science, and contribute to enhanced understanding of injury patterns
using predictive modeling along with safety experts’ perspectives with regulatory or managerial viewpoints. The
predictive models obtained from this study can be used to augment the experience of safety professionals in
agribusiness industries to improve safety intervention efforts. Keywords: Injury severity classification | Injury severity prediction | Machine learning |
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