Predicting Workplace Injuries Using Machine Learning Algorithms

Sukumar, Divya and Zhang, Ji and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Wang, Xin and Zhang, Wenbin (2020) Predicting Workplace Injuries Using Machine Learning Algorithms. In: 7th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2020), 6-9 Oct 2020, 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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Date Deposited: 22 Feb 2021 02:31
Last Modified: 26 Feb 2021 04:19
Uncontrolled Keywords: predictive modeling, machine learning, model performance
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460502 Data mining and knowledge discovery
Identification Number or DOI: https://doi.org/10.1109/DSAA49011.2020.00104
URI: http://eprints.usq.edu.au/id/eprint/41394

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