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Comparison of the performance of models showed Balanced Accuracy to be the best for SVM and SGB methods in 2-Class and SGB in 3-Class. Algorithms performed better at predicting fatal accidents compared to major and minor accidents. Results obtained revealed that, ML unveils factors contributing to severity to better address the corrective actions. Furthermore, taking action related to even some of the most significant factors in complex accidents database with many attributes can prevent majority of severe accidents. Interpretation of most significant factors identified for accident prediction suggest the following corrective measures: taking fall prevention actions, prioritizing workplace inspections based on the number of employees, and supplementing safety actions according to worker\u2019s age and experience.<\/jats:p>","DOI":"10.3233\/jifs-202099","type":"journal-article","created":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T12:14:24Z","timestamp":1617970464000},"page":"10981-10998","source":"Crossref","is-referenced-by-count":7,"title":["Comparison of machine learning methods in predicting binary and multi-class occupational accident severity"],"prefix":"10.1177","volume":"40","author":[{"given":"F\u00fcsun","family":"Recal","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, Yildiz Technical University, \u0130stanbul, Turkey"}]},{"given":"Tufan","family":"Demirel","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Yildiz 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