Predicting Workplace Injuries Using Machine Learning Algorithms
Paper
Sukumar, Divya, Zhang, Ji, Tao, Xiaohui, Wang, Xin and Zhang, Wenbin. 2020. "Predicting Workplace Injuries Using Machine Learning Algorithms." Webb, Geoff, Zhang, Zhongfei, Tseng, Vincent S., Williams, Graham, Vlachos, Michalis and Cao, Longbing (ed.) 7th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2020). Sydney, Australia 06 - 09 Oct 2020 Piscataway, United States. https://doi.org/10.1109/DSAA49011.2020.00104
Paper/Presentation Title | Predicting Workplace Injuries Using Machine Learning Algorithms |
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Presentation Type | Paper |
Authors | Sukumar, Divya (Author), Zhang, Ji (Author), Tao, Xiaohui (Author), Wang, Xin (Author) and Zhang, Wenbin (Author) |
Editors | Webb, Geoff, Zhang, Zhongfei, Tseng, Vincent S., Williams, Graham, Vlachos, Michalis and Cao, Longbing |
Journal or Proceedings Title | Proceedings 2020 IEEE 7th International Conference on Data Science and Advanced Analytics |
Number of Pages | 2 |
Year | 2020 |
Place of Publication | Piscataway, United States |
ISBN | 9781728182063 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/DSAA49011.2020.00104 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/9260021/ |
Conference/Event | 7th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2020) |
Event Details | 7th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2020) Parent IEEE International Conference on Data Science and Advanced Analytics Event Date 06 to end of 09 Oct 2020 Event Location Sydney, Australia |
Abstract | Predicting workplace injury using automated techniques opens newer possibilities in evidence-based research. This paper presents our preliminary research in a PhD project in predicting workplace incidents using machine learning algorithms. The analysis on the model performance using several mainstream machine learning algorithms including random forest, k-nearest neighbor and decision tree indicated that the general performance of the decision tree model was found to be statistically higher than that of the other two algorithms. |
Keywords | predictive modeling, machine learning, model performance |
ANZSRC Field of Research 2020 | 460502. Data mining and knowledge discovery |
Byline Affiliations | University of Southern Queensland |
Southwest Petroleum University, China | |
Carnegie Mellon University, United States | |
Institution of Origin | University of Southern Queensland |
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https://research.usq.edu.au/item/q63x0/predicting-workplace-injuries-using-machine-learning-algorithms
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