References
- Occupational Safety & Health Research Institute. Analysis of Industrial Accidents in 2019. Ulsan (Korea): Korea Occupational Safety and Health Agency; 2020. p. 7-640.
- Kang KS, Ryu HG. Predicting types of occupational accidents at construction sites in Korea using random forest model. Safety Science. 2019 Dec;120:226-36. https://doi.org/10.1016/j.ssci.2019.06.034
- Ale BJM, Bellamy LJ, Baksteen H, Damen M, Goossens LHJ, Hale AR, Mud M, Papazoglou IA, Whiston JY. Accidents in the construction industry in the netherlands: An analysis of accident reports using storybuilder. Reliability Engineering & System Safety. 2008 Oct;93(10):1523-33. https://doi.org/10.1016/j.ress.2007.09.004
- Swuste P, Frijters A, Guldenmund F. Is it possible to influence safety in the building sector?: A literature review extending fro m 1980 until the present. Safety science. 2012 Jun;50(5):1333-43. https://doi.org/10.1016/j.ssci.2011.12.036
- Liao CW, Perng YH. Data mining for occupational injuries in the Taiwan construction industry. Safety science. 2008 Aug;46(7):1091-102. https://doi.org/10.1016/j.ssci.2007.04.007
- Winge S, Albrechtsen E. Accident types and barrier failures in the construction industry. Safety science. 2018 Jun;105:158-66. https://doi.org/10.1016/j.ssci.2018.02.006
- Hosseinian SS, Torghabeh ZJ. Major theories of construction accident causation models: A literature review. International Journal of Advances in Engineering & Technology. 2012 Sep;4(2):53-66.
- Lopez-Alonso M, Ibarrondo-Davila MP, Rubio-Gamez MC, Munoz TG. The impact of health and safety investment on construction company costs. Safety science. 2013 Dec;60:151-9. https://doi.org/10.1016/j.ssci.2013.06.013
- Santana VS, Villaveces A, Bangdiwala SI, Runyan CW, Albuquerque-Oliveira PR. Workdays lost due to occupational injuries among young workers in Brazil. American journal of industrial medicine. 2012 Oct;55(10):917-25. https://doi.org/10.1002/ajim.22099. Epub 2012 Jul 27
- Manu P, Ankrah N, Proverbs D, Suresh S. An approach for deter mining the extent of contribution of construction project features to accident causation. Safety Science. 2010 Jul;48(6):687-92. https://doi.org/10.1016/j.ssci.2010.03.001
- Boden LI, Biddle EA, Spieler EA. Social and economic impacts of workplace illness and injury: current and future directions for research. American Journal of Industrial Medicine. 2001 Oct;40(4):398-402. https://doi.org/10.1002/ajim.10013
- Cho YR, Kim YC, Shin YS. Prediction model of construction safety accidents using decision tree technique. Journal of the Korea Institute of Building Construction. 2017 Jun;17(3):295-303. https://doi.org/10.5345/JKIBC.2017.17.3.295
- Kim YC, Yoo WS, Shin YS. Application of artificial neural networks to prediction of construction safety accidents. The Journal of the Korean Society of Hazard Mitigation. 2017 Fed;17(1):7-14. https://doi.org/10.9798/KOSHAM.2017.17.1.7
- Kim EJ. Prediction model for construction safety accidents using random forest. Journal of The Regional Association of Architectural Institute of Korea. 2020 Oct;22(5):81-7.
- Weil D. Valuing the economic consequences of work injury and illness: a comparison of methods and findings. American Journal of Industrial Medicine. 2001 Oct;40(4):418-37. https://doi.org/10.1002/ajim.1114
- Jeong WI, Lee KS, Jeon YI. Occupational accidents and foregone working days. Journal of Korean Economics Studies. 2011 Jun;29(2):139-74.
- Choi JW, Kim TW, Lee CS. Effects of weather factors on the work loss days of the elderly workers. Korean Journal of Construction Engineering and Management. 2019 Jan;20(1):41-51. https://doi.org/10.6106/KJCEM.2019.20.1.041
- Lee DB, Lee TY, Jo YC, Lee YS, Sim UT. Analysis of absenteeism factors of workers in manufacturing industries. Industrial Health. 1995 May;85:2-9.
- Yoon JH, Jang SR, Im HK. Occupational accident prevention policy for middle-aged and old workers according to the trend of occupational accident. Ergonomics Society of Korea 2008 Conference; 2008 May 23; Gumi-si, Korea. Seoul (Korea): Ergonomics Society of Korea; 2008. p. 8-11.
- Korea Health Industry Development Institute. Hospital management status survey - The average number of hospital stays for inpatients by type of salary [Internet]. Cheongju (Korea): Korea Health Industry Development Institute; 2015 Set 30. Available from: https://kosis.kr/statHtml/statHtml.do?orgId=358&tblId=DT_358N_H409101
- Jeong WM, Park CY, Koo JW, Roh YM. Predictors of return to work in occupational injured workers. Annals of Occupational and Environmental Medicine 2003;15(2):119-31.
- Jo JH. A study on the Causes Analysis and Preventive Measures by Disaster types in Construction Fields. Conference of Korea Safety Management and Science; 2011 Nov; Cheonan-si, South Korea. Incheon (Korea): Korea Safety Management and Science; 2011. p. 23-34.
- Ruser JW. The changing composition of lost-workday injuries. Monthly Labor Review. 1999 Jun;122(6):11-9.
- Health and Safety Executive. Working days lost in Great Britain [Internet]. Merseyside (England): Health and Safety Executive; 2017. Available from: http://www.hse.gov.uk/statistics/dayslost.htm
- Yang YS, Park JH, Lee CS. Accident risk analysis of construction workers by occupation. Journal of the Architectural Institue of Korea Structure & Construction. 2009;25(10):149-56.
- Kim SG, An HY, Lee EH. A comparative study on the changes in indicators of occupational accidents and social and economic activities in OECD countries. Ulsan-si (Korea): Occupational Safety and Health Research Institute. 2009 Dec. Report No.: 2009-72-1264.
- Kotsiantis SB. Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering. 2007 Jun;160:3-24.
- Breiman L, Friedman J, Olshen R, Stone C. Classification and regression trees. CRC press. 1984. p. 237-51.
- Geron A. Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. 1st ed. Sebastopol (CA): O'Reilly Media. 2017. p. 189-211.
- David D. Summing feature importance in Scikit-learn for a set of features [Internet]. StackExchange; 2017 [updated 2020 Sep 11; cited 2021 Feb 12]. Available from: http://bit.ly/2TYUjnu
- Breiman L. Random forests. Machine learning. 2001 Oct;45(1):5-32. https://doi.org/10.1023/A:1010933404324
- Parr T, Turgutlu K, Csiszar C, Howard J. Beware Default Random Forest Importances. explained.ai [Preprint]. 2018 [cited 2021 Feb 12]. Available from: https://explained.ai/rf-importance/
- U.S. Bureau of Labor Statistics. Employer-Reported Workplace Injuries and Illnesses (Annual) News Release [Internet]. U.S. Department of Labor, Washington (DC): U.S. Bureau of Labor Statistics; 2020 Apr 11 [updated 2020 Nov 4; cited 2021 Feb 28]. Available from: https://www.bls.gov/news.release/osh.htm