Abstract
Some studies have tried to develop predictors for fitness for work (FFW). This study assessed the question whether factors used in the occupational medical practice could predict an individual fit for work result. We used a Peruvian occupational medical examination dataset of 33347 participants. We obtained a reduced dataset of 2650. It was split into two subsets, a training dataset and a test dataset. Using the training dataset, logistic regression, decision tree, random forest, and support vector machine models were fitted, and important variables of each model were identified. Hyperparameter tuning was an important part in these non-parametric models. Also, the Area Under the Curve (AUC) metric was used for Model Selection with a 5-fold cross validation approach. The results shows the Logistic Regression as the most powerful predictor (AUC = 60.44%, Accuracy = 68.05%). It is important to notice the best variables analysis in fitness to work evaluation by a Random Forest approach. Thus, the best model was logistic regression. This also reveals that the criteria associated with the workplace and occupational clinical criteria have a low level of prediction. Further studies should be done with imbalanced data to process bigger datasets, in consequence to obtain more robust models.
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References
Murdoch, T.B., Detsky, A.S.: The inevitable application of big data to health care. JAMA 309(13), 1351–1352 (2013)
Kruse, C.S., Goswamy, R., Raval, Y.J., Marawi, S.: Challenges and opportunities of big data in health care: a systematic review. JMIR Med. Inf. 4(4), e38 (2016)
Char, D.S., Shah, N.H., Magnus, D.: Implementing machine learning in health care–addressing ethical challenges. New Engl. J. Med. 378(11), 981 (2018)
Mona, G.G., Chimbari, M.J., Hongoro, C.: A systematic review on occupational hazards, injuries and diseases among police officers worldwide: policy implications for the South African police service. J. Occup. Med. Toxicol. 14(1), 2 (2019)
Rommel, A., Varnaccia, G., Lahmann, N., Kottner, J., Kroll, L.E.: Occupational injuries in Germany: population-wide national survey data emphasize the importance of work-related factors. PLoS One 11(2), e0148798 (2016)
Saifullah, H., Li, J.: Workplace employee’s annual physical check-up and during hire on the job to increase health care-awareness perception to prevent diseases risk: a work for policy implementable option to global. Saf. Health Work 10(2), 132–140 (2018)
Cox, R.A.F., Edwards, F., Palmer, K.: Fitness for Work: The Medical Aspects. Oxford University Press, Oxford (2000)
Coggon, D., Palmer, K.T.: Assessing fitness for work and writing a “fit note". BMJ 341, c6305 (2010)
Serra, C., Rodriguez, M.C., Delclos, G.L., Plana, M., López, L.I.G., Benavides, F.G.: Criteria and methods used for the assessment of fitness for work: a systematic review. Occup. Environ. Med. 64(5), 304–312 (2007)
Foley, M., Thorley, K., Van Hout, M.C.: Assessing fitness for work: GPs judgment making. Eur. J. Gen. Pract. 19(4), 230–236 (2013)
Mahmud, N., et al.: Pre-employment examinations for preventing occupational injury and disease in workers. Cochrane Database Syst. Rev. (12), 1–46 (2010). https://doi.org/10.1002/14651858.CD008881. Article no. CD008881
Raschka, S.: Model evaluation, model selection, and algorithm selection in machine learning (2018)
Wong, J., Manderson, T., Abrahamowicz, M., Buckeridge, D.L., Tamblyn, R.: Can hyperparameter tuning improve the performance of a super learner? a case study. Epidemiol. (Cambridge, Mass.) 30(4), 521 (2019)
Lee, J., Kim, H.R.: Prediction of return-to-original-work after an industrial accident using machine learning and comparison of techniques. J. Korean Med. Sci. 33(19), 1–12 (2018)
Lindholm, A., Wahlström, N., Lindsten, F., Schön, T.B.: Supervised machine learning. http://www.it.uu.se/edu/course/homepage/sml/literature/lecture_notes.pdf. Accessed 31 May 2019
Cowell, J.: Guidelines for fitness-to-work examinations. CMAJ: Can. Med. Assoc. J. 135(9), 985 (1986)
Zhou, Z., Hooker, G.: Unbiased measurement of feature importance in tree-based methods. arXiv preprint arXiv:1903.05179 (2019)
Konno, T., Iwazume, M.: Pseudo-feature generation for imbalanced data analysis in deep learning (2018)
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Charapaqui-Miranda, S., Arapa-Apaza, K., Meza-Rodriguez, M., Chacon-Torrico, H. (2020). Comparing Predictive Machine Learning Algorithms in Fit for Work Occupational Health Assessments. In: Lossio-Ventura, J.A., Condori-Fernandez, N., Valverde-Rebaza, J.C. (eds) Information Management and Big Data. SIMBig 2019. Communications in Computer and Information Science, vol 1070. Springer, Cham. https://doi.org/10.1007/978-3-030-46140-9_21
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