[
Chena, H., Luoa, X., 2016. Severity Prediction Models of Falling Risk for Workers et Height. Procedia Engineering, 164, 439-445, DOI: 10.1016/j.proeng.2016.11.642.
]Search in Google Scholar
[
Ciarapica, F.E., Giacchetta, G., 2009. Classification and prediction of occupational injury risk using soft computing techniques: An Italian study, Safety Science, 47(1), 36-49, DOI: 10.1016/j.ssci.2008.01.006.
]Search in Google Scholar
[
Delen, D., Sharda, R., Bessonov, M., 2005. Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks, Accident Analysis and Prevention, 38(3), 434-444, DOI: 10.1016/j.aap.2005.06.024.
]Search in Google Scholar
[
Karra, V.K., 2005. Analysis of non-fatal and fatal injury rates for mine operator and contractor employees and the influence of work location, Journal of Safety Research 36(5), 413-421, DOI: 10.1016/j.jsr.2005.08.002.
]Search in Google Scholar
[
Rivas, T., Paz, M., Martin, J.E., Matias, J.A., Garcia, J.F., Taboada, J., 2011. Explaining and predicting workplace accidents using data-mining techniques, Reliability Engineering and System Safety 96(7), 739-747, DOI: 10.1016/j.ress. 2011.03.006.
]Search in Google Scholar
[
Sari, M., Duzgun, H.S.B., Karpuz, C., Selcuk, A.S., 2004. Accident analysis of two Turkish underground coal mines, Safety Science, 4298, 675-690, DOI: 10.1016/j. ssci.2003.11.002.
]Search in Google Scholar
[
Sarkar, S., Vinay, S., Raj, R., Maiti, J., Mitra, P., 2019. Application of optimized machine learning techniques for prediction of occupational accidents, Computers and Operations Research, 106, 210-224, DOI: 10.1016/j.cor.2018.02.021.
]Search in Google Scholar
[
Stojadinovic, S., Svrkuta, I., Petrović, D., Denic, M., Pantovic, R., Milić, V., 2012. Mining injuries in Serbian underground coal mines – A 10-year study, Injury 43(12), 2001-2005, DOI: 10.1016/j.injury.2011.08.018.
]Search in Google Scholar
[
Stylianou, N., Akbarov, A., Kontopantelis, E., Buchan, I, Dunn, K.W., 2015. Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches, Burns 41(5), 925-934. DOI: 10.1016/j.burns.2015.03.016.
]Search in Google Scholar
[
Vallmuur, K., 2015. Machine learning approaches to analysing textual injury surveillance data: A systematic review, Accident Analysis and Prevention, 79, 41-49. DOI: 10.1016/j.aap.2015.03.018.
]Search in Google Scholar
, 