Utilization of Machine Learning in Supporting Occupational Safety and Health Decisions in Hospital Workplace


  • K. Koklonis Biomedical Engineering Laboratory, National Technical University of Athens, Greece https://orcid.org/0000-0002-3058-5017
  • M. Sarafidis Biomedical Engineering Laboratory, National Technical University of Athens, Greece https://orcid.org/0000-0003-1921-6525
  • M. Vastardi Metaxa Cancer Hospital of Piraeus, Greece
  • D. Koutsouris Biomedical Engineering Laboratory, National Technical University of Athens, Greece


The prediction of possible future incidents or accidents and the efficiency assessment of the Occupational Safety and Health (OSH) interventions are essential for the effective protection of healthcare workers, as the occupational risks in their workplace are multiple and diverse. Machine learning algorithms have been utilized for classifying post-incident and post-accident data into the following 5 classes of events: Needlestick/Cut, Falling, Incident, Accident, and Safety. 476 event reports from Metaxa Cancer Hospital (Greece), during 2014-2019, were used to train the machine learning models. The developed models showed high predictive performance, with area under the curve range 0.950-0.990 and average accuracy of 93% on the 10-fold cross set, compared to the safety engineer’s study reports. The proposed DSS model can contribute to the prediction of incidents or accidents and efficiency evaluation of OSH interventions.


occupational health and safety, osh, machine learning, hospital workplace


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D. Elsler, J. Takala, and J. Remes, “An international comparison of the cost of work-related accidents and illnesses,” European Agency for Safety and Health at Work, 2017.

“Good OSH is good for business,” EU-OSHA. https://osha.europa.eu/el/themes/good-osh-is-good-for-business (accessed May 26, 2021).

K. Cosic, S. Popovic, M. Sarlija, I. Kesedzic, and T. Jovanovic, “Artificial intelligence in prediction of mental health disorders induced by the COVID-19 pandemic among health care workers,” Croatian Medical Journal, vol. 61, no. 3, pp. 279–288, Jun. 2020.

K. Dimoulas, G. Kollias, C. Bagavos, and T. Ganetaki, Work and health problems in Greece. Athens, Greece: INE-GSEE Work Institute, 2015.

Hospital Inventory 2018. Athens, Greece: Hellenic Statistical Authority, 2020.

Survey on Accidents at Work, 2018. Athens, Greece: Hellenic Statistical Authority, 2020.

S. Sarkar and J. Maiti, “Machine learning in occupational accident analysis: A review using science mapping approach with citation network analysis,” Safety Science, vol. 131, p. 104900, Nov. 2020.

F. Siddiqui, M. A. Akhund, A. H. Memon, A. R. Khoso, and H. U. Imad, “Health and Safety Issues of Industry Workmen,” Engineering, Technology & Applied Science Research, vol. 8, no. 4, pp. 3184–3188, Aug. 2018.

S. Y. Far, R. Mirzaei, M. B. Katrini, M. Haghshenas, and Z. Sayahi, “Assessment of Health, Safety and Environmental Risks of Zahedan City Gasoline Stations,” Engineering Technology & Applied Science Research, vol. 8, no. 2, pp. 2689–2692, 2018.

S. J. Bertke, A. R. Meyers, S. J. Wurzelbacher, J. Bell, M. L. Lampl, and D. Robins, “Development and evaluation of a Naïve Bayesian model for coding causation of workers’ compensation claims,” Journal of Safety Research, vol. 43, no. 5, pp. 327–332, Dec. 2012.

G. Nanda, K. M. Grattan, M. T. Chu, L. K. Davis, and M. R. Lehto, “Bayesian decision support for coding occupational injury data,” Journal of Safety Research, vol. 57, pp. 71–82, Jun. 2016.

J. E. M. E. Martin, J. T.-G. Taboada-Garcia, S. G. Gerassis, A. S. Saavedra, and R. Martinez-Alegria, “Bayesian network analysis of accident risk in information-deficient scenarios,” Revista de la Construcción. Journal of Construction, vol. 16, no. 3, pp. 439–446, 2017.

A. P. C. Chan, F. K. W. Wong, C. K. H. Hon, and T. N. Y. Choi, “A Bayesian Network Model for Reducing Accident Rates of Electrical and Mechanical (E&M) Work,” International Journal of Environmental Research and Public Health, vol. 15, no. 11, Nov. 2018, Art. no. 2496.

L. Sanmiquel, M. Bascompta, J. M. Rossell, H. F. Anticoi, and E. Guash, “Analysis of Occupational Accidents in Underground and Surface Mining in Spain Using Data-Mining Techniques,” International Journal of Environmental Research and Public Health, vol. 15, no. 3, Mar. 2018, Art. no. 462.

A. Soltanzadeh, I. Mohammadfam, S. Mahmoudi, B. A. Savareh, and A. M. Arani, “Analysis and forecasting the severity of construction accidents using artificial neural network,” Safety promotion and injury prevention (Tehran), vol. 4, no. 3, pp. 185–192, 2016.

D. A. Patel and K. N. Jha, “Neural Network Approach for Safety Climate Prediction,” Journal of Management in Engineering, vol. 31, no. 6, Nov. 2015, Art. no. 05014027.

A. M. Abubakar, H. Karadal, S. W. Bayighomog, and E. Merdan, “Workplace injuries, safety climate and behaviors: application of an artificial neural network,” International Journal of Occupational Safety and Ergonomics, vol. 26, no. 4, pp. 651–661, Oct. 2020.

F. A. Moayed and R. L. Shell, “Application of Artificial Neural Network Models in Occupational Safety and Health Utilizing Ordinal Variables,” The Annals of Occupational Hygiene, vol. 55, no. 2, pp. 132–142, Mar. 2011.

I. Mohammadfam, A. Soltanzadeh, A. Moghimbeigi, and B. A. Savareh, “Use of Artificial Neural Networks (ANNs) for the Analysis and Modeling of Factors That Affect Occupational Injuries in Large Construction Industries,” Electronic Physician, vol. 7, no. 7, pp. 1515–1522, Nov. 2015.

S. Sarkar, S. Vinay, R. Raj, J. Maiti, and P. Mitra, “Application of optimized machine learning techniques for prediction of occupational accidents,” Computers & Operations Research, vol. 106, pp. 210–224, Jun. 2019.

J. Bao, J. Johansson, and J. Zhang, “An Occupational Disease Assessment of the Mining Industry’s Occupational Health and Safety Management System Based on FMEA and an Improved AHP Model,” Sustainability, vol. 9, no. 1, Jan. 2017, Art. no. 94.

H. R. S. A. Mard, A. Estiri, P. Hadadi, and M. S. A. Mard, “Occupational risk assessment in the construction industry in Iran,” International Journal of Occupational Safety and Ergonomics, vol. 23, no. 4, pp. 570–577, Oct. 2017.

L. Comberti, M. Demichela, G. Baldissone, G. Fois, and R. Luzzi, “Large Occupational Accidents Data Analysis with a Coupled Unsupervised Algorithm: The S.O.M. K-Means Method. An Application to the Wood Industry,” Safety, vol. 4, no. 4, Dec. 2018, Art. no. 51.

N. D. Nath, T. Chaspari, and A. H. Behzadan, “Automated ergonomic risk monitoring using body-mounted sensors and machine learning,” Advanced Engineering Informatics, vol. 38, pp. 514–526, Oct. 2018.

F. Davoudi Kakhki, S. A. Freeman, and G. A. Mosher, “Utilization of Machine Learning in Analyzing Post-incident State of Occupational Injuries in Agro-Manufacturing Industries,” in Advances in Safety Management and Human Performance, P. M. Arezes and R. L. Boring, Eds. New York, NY, USA: Springer, 2020, pp. 3–9.

S. D. Mwmc et al., “Ethical Considerations of Using Machine Learning for Decision Support in Occupational Health: An Example Involving Periodic Workers’ Health Assessments.,” Journal of Occupational Rehabilitation, vol. 30, no. 3, pp. 343–353, Sep. 2020.

F. Ladstatter, E. Garrosa, B. Moreno-Jimenez, V. Ponsoda, J. M. R. Aviles, and J. Dai, “Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses,” Ergonomics, vol. 59, no. 2, pp. 207–221, Feb. 2016.

Y.-H. Kim and M.-H. Jung, “Effect of occupational health nursing practice on musculoskeletal pains among hospital nursing staff in South Korea,” International Journal of Occupational Safety and Ergonomics, vol. 22, no. 2, pp. 199–206, Apr. 2016.

A. Fonseca, I. Abreu, M. J. Guerreiro, C. Abreu, R. Silva, and N. Barros, “Indoor Air Quality and Sustainability Management—Case Study in Three Portuguese Healthcare Units,” Sustainability, vol. 11, no. 1, Jan. 2019, Art. no. 101.

S. Lin, N. Chaiear, J. Khiewyoo, B. Wu, and N. P. Johns, “Preliminary Psychometric Properties of the Chinese Version of the Work-Related Quality of Life Scale-2 in the Nursing Profession,” Safety and Health at Work, vol. 4, no. 1, pp. 37–45, Mar. 2013.

W. Turnberg and W. Daniell, “Evaluation of a healthcare safety climate measurement tool,” Journal of Safety Research, vol. 39, no. 6, pp. 563–568, Jan. 2008.

A. K. Celik, E. Oktay, and K. Cebi, “Analysing workplace violence towards health care staff in public hospitals using alternative ordered response models: the case of north-eastern Turkey,” International Journal of Occupational Safety and Ergonomics, vol. 23, no. 3, pp. 328–339, Jul. 2017.

M. Stefanovic, D. Tadic, M. Djapan, and I. Macuzic, “Software for Occupational Health and Safety Risk Analysis Based on a Fuzzy Model,” International Journal of Occupational Safety and Ergonomics, vol. 18, no. 2, pp. 127–136, Jan. 2012.

A. Sklad, “Assessing the impact of processes on the Occupational Safety and Health Management System’s effectiveness using the fuzzy cognitive maps approach,” Safety Science, vol. 117, pp. 71–80, Aug. 2019.

V. Ravuri et al., “Group-specific models of healthcare workers’ well-being using iterative participant clustering,” in Second International Conference on Transdisciplinary AI, Irvine, CA, USA, Sep. 2020, pp. 115–118.

K. Vallmuur, “Machine learning approaches to analysing textual injury surveillance data: A systematic review,” Accident Analysis & Prevention, vol. 79, pp. 41–49, Jun. 2015.

“Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) (Text with EEA relevance).” Publications Office of the European Union, Apr. 27, 2016.

“Home - Weka Wiki,” The University of Waikato. https://waikato.github.io/weka-wiki/ (accessed May 27, 2021).

“Memorandum on Occupational Risk Assessment.” Directorate-General for Employment in Labor Relations and Social Affairs (DG V) of the European Union, 1997.

“Occupational Risk Assessment.” Technical Chamber of Greece, 2001.

S. Drivas, K. Zorba, and T. Koukoulaki, Methodological guide for the assessment and prevention of occupational risk. Athens, Greece: Hellenic Institute of Occupational Health and Safety, 2000.

P. Bountris et al., “An Intelligent Clinical Decision Support System for Patient-Specific Predictions to Improve Cervical Intraepithelial Neoplasia Detection,” BioMed Research International, vol. 2014, 2014.

S. Chen, G. I. Webb, L. Liu, and X. Ma, “A novel selective naïve Bayes algorithm,” Knowledge-Based Systems, vol. 192, Mar. 2020, Art. no. 105361.

K. Koutroumbas and S. Theodoridis, Pattern Recognition, 4th ed. London, UK: Elsevier, 2008.

M. A. Burhanuddin, R. Ismail, N. Izzaimah, A. A.-J. Mohammed, and N. Zainol, “Analysis of Mobile Service Providers Performance Using Naive Bayes Data Mining Technique,” International Journal of Electrical & Computer Engineering, vol. 8, no. 6, pp. 5153–5161, 2018.

R. Shinde, S. Arjun, P. Patil, and J. Waghmare, “An Intelligent Heart Disease Prediction System Using K-Means Clustering and Naïve Bayes Algorithm,” International Journal of Computer Science and Information Technologies, vol. 6, no. 1, pp. 637–639, 2015.

S. J. Russell, P. Norvig, S. Russell, and Russell, Artificial intelligence: A Modern Approach. New Jersey, USA: Prentice Hall, 2010.

D. Michie, D. J. Spiegelhalter, C. C. Taylor, and J. Campbell, Eds., Machine learning, neural and statistical classification. New York, NY, USA: Ellis Horwood, 1995.

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. Hoboken New Jersey, USA: Wiley, 2001.

I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed. Burlington, MA, USA: Morgan Kaufmann, 2011.

S. M. Weiss and C. A. Kulikowski, Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems. San Mateo, CA, USA: Morgan Kaufmann, 1990.

B. D. Ripley, Pattern Recognition and Neural Networks. Cambridge, MA, USA: Cambridge University Press, 2008.

A. Nola et al., “Occupational accidents in temporary work,” La Medicina Del Lavoro, vol. 92, no. 4, pp. 281–285, Aug. 2001.

T. Fawcett, “An introduction to ROC analysis,” Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, Jun. 2006.

J. López-García, M. Saldaña, S. Herrero, and J. Gutiérrez, “Bayesian network analysis of the influence of labour market variables on accident rates of workers in Spain,” in Risk, Reliability and Safety: Innovating Theory and Practice: Proceedings of ESREL 2016, Glasgow, UK, Sep. 2016, pp. 1660–1667.

J. A. Hanley and B. J. McNeil, “The meaning and use of the area under a receiver operating characteristic (ROC) curve.,” Radiology, vol. 143, no. 1, pp. 29–36, Apr. 1982.

S. Alvarez, “An exact analytical relation among recall, precision, and classification accuracy in information retrieval,” Boston College, Boston, MA, USA, Technical Report BCCS-02-01 (2002): 1-22, Jan. 2002.

R. Burduk, “Classification Performance Metric for Imbalance Data Based on Recall and Selectivity Normalized in Class Labels,” arXiv:2006.13319 [cs, stat], Jun. 2020, Accessed: May 26, 2021. [Online]. Available: http://arxiv.org/abs/2006.13319.

O. Ug, S. Wd, S. M, and P. A, “Improve Process Safety with Near-Miss Analysis,” Chemical Engineering Progress, vol. 109, no. 5, pp. 20–27, 2013.

M. G. Gnoni, S. Andriulo, G. Maggio, and P. Nardone, “‘Lean occupational’ safety: An application for a Near-miss Management System design,” Safety Science, vol. 53, pp. 96–104, Mar. 2013.

E. Alexopoulos, Greek and International experience of accidents at work and occupational diseases of hospital employees. Guide to Occupational Risk Assessment and Prevention. Athens, Greece: EL.Y.A., 2007.

“Circular 45/24-06-2010: Occupational Accident 2010.” Social Security Institution, 2010.

G. Reniers and T. Brijs, “An Overview of Cost-benefit Models/Tools for Investigating Occupational Accidents,” Chemical Engineering Transactions, vol. 36, pp. 43–48, Apr. 2014.

Health and Safety Executive, “Risk management: Expert guidance - ALARP at a glance.” https://www.hse.gov.uk/managing/theory/alarpglance.htm (accessed May 26, 2021).

S. J. Bertke, A. R. Meyers, S. J. Wurzelbacher, J. Bell, M. L. Lampl, and D. Robins, “Development and evaluation of a Naïve Bayesian model for coding causation of workers’ compensation claims,” Journal of Safety Research, vol. 43, no. 5, pp. 327–332, Dec. 2012.

K. Koklonis, A. Anastasiou, O. Petropoulou, S. Pitoglou, D. Iliopoulou, and D. Koutsouris, “Utilizing Key Item Method to Manage Musculoskeletal Disorders in a Hospital Workplace,” in 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Berlin, Germany, Jul. 2019, pp. 3420–3423.


How to Cite

K. Koklonis, M. Sarafidis, M. Vastardi, and D. Koutsouris, “Utilization of Machine Learning in Supporting Occupational Safety and Health Decisions in Hospital Workplace”, Eng. Technol. Appl. Sci. Res., vol. 11, no. 3, pp. 7262–7272, Jun. 2021.


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