Abstract
Due to the high frequency and costs of occupational incidents in agro-manufacturing operations, as well as substantial impact of occupational injuries on labor-market outcomes, predicting the post-incident state of an injury and identifying its contributory factors is vital to protect workers, improve workplace safety, and reduce overall costs of injuries. This study evaluates the performance of machine learning algorithms in classifying post-incident outcomes of occupational injuries in agro-manufacturing operations. Injury factors extracted from 14,000 workers’ compensation claims recorded between 2008 and 2016 in the Midwest region of the United States were used to develop machine learning models. The models predicted incident outcomes based on injury details with an overall accuracy rate of 78%, and high accuracy rate for medical post-incident state (97–98%). The results emphasize the significance of quantitative analysis of empirical injury data in safety science, and contributes to enhanced understanding of occupational incidents root causes using predictive modeling along with safety experts’ perspective.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boden, L.I., O’Leary, P.K., Applebaum, K.M., Tripodis, Y.: The impact of non-fatal workplace injuries and illnesses on mortality. Am. J. Ind. Med. (2016). https://doi.org/10.1002/ajim.22632
Tsoukalas, V.D., Fragiadakis, N.G.: Prediction of occupational risk in the shipbuilding industry using multivariable linear regression and genetic algorithm analysis. Saf. Sci. 83, 12–22 (2016). https://doi.org/10.1016/j.ssci.2015.11.010
Gavious, A., Mizrahi, S., Shani, Y., Minchuk, Y.: The costs of industrial accidents for the organization: developing methods and tools for evaluation and cost-benefit analysis of investment in safety. J. Loss Prev. Process Ind. (2009). https://doi.org/10.1016/j.jlp.2009.02.008
Kakhki, F.D., Freeman, S.A., Mosher, G.A.: Evaluating machine learning performance in predicting injury severity in agribusiness industries. Saf. Sci. (2019). https://doi.org/10.1016/j.ssci.2019.04.026
Altunkaynak, B.: A statistical study of occupational accidents in the manufacturing industry in Turkey. Int. J. Ind. Ergon. 66, 101–109 (2018). https://doi.org/10.1016/j.ergon.2018.02.012
Robert, K., Elisabeth, Q., Josef, B.: Analysis of occupational accidents with agricultural machinery in the period 2008–2010 in Austria. Saf. Sci. (2015). https://doi.org/10.1016/j.ssci.2014.10.004
Field, W.E., Heber, D.J., Riedel, S.M., Wettschurack, S.W., Roberts, M.J., Grafft, L.M.J.: Worker hazards associated with the use of grain vacuum systems. J. Agric. Saf. Health (2014). https://doi.org/10.13031/jash.20.9989
Zylbersztajn, D.: Agribusiness systems analysis: origin, evolution and research perspectives. Rev. Adm. (2017). https://doi.org/10.1016/j.rausp.2016.10.004
Kakhki, F.D., Freeman, S.A., Mosher, G.A.: Use of logistic regression to identify factors influencing the post-incident state of occupational injuries in agribusiness operations. Appl. Sci. (2019). https://doi.org/10.3390/app9173449
Sears, J.M., Blanar, L., Bowman, S.M.: Predicting work-related disability and medical cost outcomes: a comparison of injury severity scoring methods. Injury (2014). https://doi.org/10.1016/j.injury.2012.12.024
Zakiei, A., Kiani, N., Morovati, F., Komasi, S.: Classification of various types of disability and determining their predictive causes in western Iran. Clin. Epidemiol. Glob. Health (2018). https://doi.org/10.1016/j.cegh.2018.11.003
Davoudi Kakhki, F., Freeman, S.A., Mosher, G.A.: Use of neural networks to identify safety prevention priorities in agro-manufacturing operations within commercial grain elevators. Appl. Sci. 9(21), 4690 (2019). https://doi.org/10.3390/app9214690
Ramaswamy, S.K., Mosher, G.A.: Using workers’ compensation claims data to characterize occupational injuries in the biofuels industry. Saf. Sci. (2018). https://doi.org/10.1016/j.ssci.2017.12.014
Kakhki, F.D., Freeman, S.A., Mosher, G.A.: Segmentation of severe occupational incidents in agribusiness industries using latent class clustering. Appl. Sci. (2019). https://doi.org/10.3390/app9183641
Meyers, A.R., Al-Tarawneh, I.S., Wurzelbacher, S.J., Bushnell, P.T., Lampl, M.P., Bell, J.L., Bertke, S.J., Robins, D.C., Tseng, C.Y., Wei, C., Raudabaugh, J.A., Schnorr, T.M.: Applying machine learning to workers’ compensation data to identify industry-specific ergonomic and safety prevention priorities: Ohio, 2001 to 2011. J. Occup. Environ. Med. (2018). https://doi.org/10.1097/JOM.0000000000001162
Tremblay, A., Badri, A.: A novel tool for evaluating occupational health and safety performance in small and medium-sized enterprises: the case of the Quebec forestry/pulp and paper industry. Saf. Sci. (2018). https://doi.org/10.1016/j.ssci.2017.09.017
Leigh, J.P.: Economic burden of occupational injury and illness in the United States. Milbank Q. (2011). https://doi.org/10.1111/j.1468-0009.2011.00648.x
Khanzode, V.V., Maiti, J., Ray, P.K.: Occupational injury and accident research: a comprehensive review (2012). https://doi.org/10.1016/j.ssci.2011.12.015
Lord, D., Mannering, F.: The statistical analysis of crash-frequency data: a review and assessment of methodological alternatives. Transp. Res. Part A Policy Pract. (2010). https://doi.org/10.1016/j.tra.2010.02.001
Wurzelbacher, S.J., Al-Tarawneh, I.S., Meyers, A.R., Bushnell, P.T., Lampl, M.P., Robins, D.C., Tseng, C.Y., Wei, C., Bertke, S.J., Raudabaugh, J.A., Haviland, T.M., Schnorr, T.M.: Development of methods for using workers’ compensation data for surveillance and prevention of occupational injuries among state-insured private employers in Ohio. Am. J. Ind. Med. (2016). https://doi.org/10.1002/ajim.22653
Jacinto, C., Canoa, M., Soares, C.G.: Workplace and organisational factors in accident analysis within the Food Industry. Saf. Sci. (2009). https://doi.org/10.1016/j.ssci.2008.08.002
Bevilacqua, M., Ciarapica, F.E., Giacchetta, G.: Industrial and occupational ergonomics in the petrochemical process industry: a regression trees approach. Accid. Anal. Prev. (2008). https://doi.org/10.1016/j.aap.2008.03.012
Mistikoglu, G., Gerek, I.H., Erdis, E., Usmen, P.E.M., Cakan, H., Kazan, E.E.: Decision tree analysis of construction fall accidents involving roofers. Expert Syst. Appl. (2015). https://doi.org/10.1016/j.eswa.2014.10.009
Kang, K., Ryu, H.: Predicting types of occupational accidents at construction sites in Korea using random forest model. Saf. Sci. (2019). https://doi.org/10.1016/j.ssci.2019.06.034
Koyuncugil, A.S., Ozgulbas, N.: Financial early warning system model and data mining application for risk detection. Expert Syst. Appl. (2012). https://doi.org/10.1016/j.eswa.2011.12.021
Shirali, G.A., Noroozi, M.V., Malehi, A.S.: Predicting the outcome of occupational accidents by CART and CHAID methods at a steel factory in Iran. J. Public health Res. (2018). https://doi.org/10.4081/jphr.2018.1361
Zheng, Z., Lu, P., Lantz, B.: Commercial truck crash injury severity analysis using gradient boosting data mining model. J. Saf. Res. (2018). https://doi.org/10.1016/j.jsr.2018.03.002
De Oña, J., López, G., Abellán, J.: Extracting decision rules from police accident reports through decision trees. Accid. Anal. Prev. (2013). https://doi.org/10.1016/j.aap.2012.09.006
Kashani, A.T., Shariat Mohaymany, A., Ranjbari, A.: Analysis of factors associated with traffic injury severity on rural roads in Iran. J. Inj. Violence Res. (2012). https://doi.org/10.5249/jivr.v4i1.67
Sarkar, S., Raj, R., Vinay, S., Maiti, J., Pratihar, D.K.: An optimization-based decision tree approach for predicting slip-trip-fall accidents at work. Saf. Sci. (2019). https://doi.org/10.1016/j.ssci.2019.05.009
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Davoudi Kakhki, F., Freeman, S.A., Mosher, G.A. (2020). Utilization of Machine Learning in Analyzing Post-incident State of Occupational Injuries in Agro-Manufacturing Industries. In: Arezes, P., Boring, R. (eds) Advances in Safety Management and Human Performance. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1204. Springer, Cham. https://doi.org/10.1007/978-3-030-50946-0_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-50946-0_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-50945-3
Online ISBN: 978-3-030-50946-0
eBook Packages: EngineeringEngineering (R0)