Data mining in occupational safety and health: a systematic mapping and roadmap
Beatriz Lavezo dos Reis; Ana Caroline Francisco da Rosa; Ageu de Araujo Machado; Simone Luzia Santana Sambugaro Wencel; Gislaine Camila Lapasini Leal; Edwin Vladimir Cardoza Galdamez; Rodrigo Clemente Thom de Souza
Abstract
Keywords
References
Abad, A., Gerassis, S., Saavedra, Á., Giráldez, E., García, J. F., & Taboada, J. (2019). A Bayesian assessment of occupational health surveillance in workers exposed to silica in the energy and construction industry.
Afzal, W., Torkar, R., & Feldt, R. (2009). A systematic review of search-based testing for non-functional system properties.
Akboğa, Ö., & Baradan, S. (2017). Safety in ready mixed concrete industry: descriptive analysis of injuries and development of preventive measures.
Antwi-Afari, M. F., Li, H., Yu, Y., & Kong, L. (2018). Wearable insole pressure system for automated detection and classification of awkward working postures in construction workers.
Badri, A., Boudreau-Trudel, B., & Souissi, A. S. (2018). Occupational health and safety in the industry 4.0 era: a cause for major concern?
Baghdadi, A. (2018). Application of inertial measurement units for advanced safety surveillance system using individualized sensor technology (ASSIST): a data fusion and machine learning approach. In
Bertke, S. J., Meyers, A. R., Wurzelbacher, S. J., Bell, J., Lampl, M. L., & Robins, D. (2012). Development and evaluation of a Naïve Bayesian model for coding causation of workers’ compensation claims.
Bevilacqua, M., Ciarapica, F. E., & Giacchetta, G. (2008). Industrial and occupational ergonomics in the petrochemical process industry: a regression trees approach.
Bohanec, M., & Delibašić, B. (2015). Data-mining and expert models for predicting injury risk in ski resorts.
Bonneterre, V., Bicout, D. J., & De Gaudemaris, R. (2012). Application of pharmacovigilance methods in occupational health surveillance: comparison of seven disproportionality metrics.
Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection.
Chen, H., Hou, C., Zhang, L., & Li, S. (2020). Comparative study on the strands of research on the governance model of international occupational safety and health issues.
Cheng, C.-W., Leu, S.-S., Cheng, Y.-M., Wu, T.-C., & Lin, C.-C. (2012). Applying data mining techniques to explore factors contributing to occupational injuries in Taiwan’s construction industry.
Cheng, C.-W., Lin, C.-C., & Leu, S.-S. (2010). Use of association rules to explore cause-effect relationships in occupational accidents in the Taiwan construction industry.
Cheng, C.-W., Yao, H.-Q., & Wu, T.-C. (2013). Applying data mining techniques to analyze the causes of major occupational accidents in the petrochemical industry.
Chokor, A., Naganathan, H., Chong, W. K., & Asmar, M. E. (2016). Analyzing Arizona OSHA injury reports using unsupervised machine learning.
Ciarapica, F. E., & Giacchetta, G. (2009). Classification and prediction of occupational injury risk using soft computing techniques: An Italian study.
Comberti, L., Baldissone, G., & Demichela, M. (2015). Workplace accidents analysis with a coupled clustering methods: S.O.M. and K-means algorithms.
Comberti, L., Demichela, M., & Baldissone, G. (2018). A combined approach for the analysis of large occupational accident databases to support accident-prevention decision making.
Del Pozo-Antúnez, J. J., Ariza-Montes, A., Fernández-Navarro, F., & Molina-Sánchez, H. (2018). Effect of a job demand-control-social support model on accounting professionals’ health perception.
Di Noia, A., Martino, A., Montanari, P., & Rizzi, A. (2019). Supervised machine learning techniques and genetic optimization for occupational diseases risk prediction.
Dybå, T., Dingsøyr, T., & Hanssen, G. K. (2007). Applying systematic reviews to diverse study types: an experience report. In
Fayyad, U., Piatetsky-shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases.
Gerassis, S., Martín, J. E., García, J. T., Saavedra, A., & Taboada, J. (2017). Bayesian decision tool for the analysis of occupational accidents in the construction of embankments.
Goh, Y. M., & Ubeynarayana, C. U. (2017). Construction accident narrative classification: an evaluation of text mining techniques.
Gross, D. P., Zhang, J., Steenstra, I., Barnsley, S., Haws, C., Amell, T., McIntosh, G., Cooper, J., & Zaiane, O. (2013). Development of a computer-based clinical decision support tool for selecting appropriate rehabilitation interventions for injured workers.
Hajakbari, M. S., & Minaei-Bidgoli, B. (2014). A new scoring system for assessing the risk of occupational accidents: A case study using data mining techniques with Iran’s Ministry of Labor data.
Heo, S.-J., Kim, Y., Yun, S., Lim, S.-S., Kim, J., Nam, C.-M., Park, E.-C., Jung, I., & Yoon, J.-H. (2019). Deep learning algorithms with demographic information help to detect tuberculosis in chest radiographs in annual workers’ health examination data.
Hicks, G., Buttigieg, D., & De Cieri, H. (2016). Safety climate, strain and safety outcomes.
Jiang, H., Cai, Y., Zeng, X., & Huang, M. (2018). Does background really matter? Worker activity recognition in unconstrained construction environment. In
Jocelyn, S., Ouali, M.-S., & Chinniah, Y. (2018). Estimation of probability of harm in safety of machinery using an investigation systemic approach and Logical Analysis of Data.
Kakhki, F. D., Freeman, S. A., & Mosher, G. A. (2019). Evaluating machine learning performance in predicting injury severity in agribusiness industries.
Kang, K., & Ryu, H. (2019). Predicting types of occupational accidents at construction sites in Korea using random forest model.
Kao, H., Hosseinmardi, H., Yan, S., Hasan, M., Narayanan, S., Lerman, K., & Ferrara, E. (2018). Discovering latent psychological structures from self-report assessments of hospital workers. In
Keele, S. (2007).
Khosrowabadi, N., & Ghousi, R. (2019). Decision support approach to occupational safety using data mining.
Kitchenham, B. (2004).
Kitchenham, B., Budgen, D., & Brereton, P. (2011). Using mapping studies as the basis for further research: a participant-observer case study.
Krishna, O. B., Maiti, J., Ray, P. K., & Mandal, S. (2015). Assessment of risk of musculoskeletal disorders among crane operators in a steel plant: a data mining-based analysis.
Lee, J., & Kim, H.-R. (2018). Prediction of return-to-original-work after an industrial accident using machine learning and comparison of techniques.
Liao, C.-W., & Perng, Y.-H. (2008). Data mining for occupational injuries in the Taiwan construction industry.
Luo, X., Yang, X., Wang, W., Chang, X., Wang, X., & Zhao, Z. (2016). A novel hidden danger prediction method in cloud-based intelligent industrial production management using timeliness managing extreme learning machine.
Marucci-Wellman, H. R., Corns, H. L., & Lehto, M. R. (2017). Classifying injury narratives of large administrative databases for surveillance: a practical approach combining machine learning ensembles and human review.
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. (2018). Applying machine learning to workers’ compensation data to identify industry-specific ergonomic and safety prevention priorities: Ohio, 2001 to 2011.
Mistikoglu, G., Gerek, I. H., Erdis, E., Mumtaz Usmen, P. E., Cakan, H., & Kazan, E. E. (2015). Decision tree analysis of construction fall accidents involving roofers.
Nanda, G., Grattan, K. M., Chu, M. T., Davis, L. K., & Lehto, M. R. (2016). Bayesian decision support for coding occupational injury data.
Nenonen, N. (2013). Analysing factors related to slipping, stumbling, and falling accidents at work: Application of data mining methods to Finnish occupational accidents and diseases statistics database.
Olsen, G. F., Brilliant, S. S., Primeaux, D., & Najarian, K. (2009). Signal processing and machine learning for real-time classification of ergonomic posture with unobtrusive on-body sensors; application in dental practice. In
Palamara, F., Piglione, F., & Piccinini, N. (2011). Self-organizing map and clustering algorithms for the analysis of occupational accident databases.
Paliyawan, P., Nukoolkit, C., & Mongkolnam, P. (2014). Office workers syndrome monitoring using kinect. In
Paternoster, N., Giardino, C., Unterkalmsteiner, M., Gorschek, T., & Abrahamsson, P. (2014). Software development in startup companies: a systematic mapping study.
Pekel, E., Akschir, Z. D., Meto, B., Akleylek, S., & Kilic, E. (2018). A Bayesian network application in occupational health and safety. In
Petersen, K., Vakkalanka, S., & Kuzniarz, L. (2015). Guidelines for conducting systematic mapping studies in software engineering: an update.
Qu, Z. (2009). Application of data mining in classification analysis of safety accidents based on alternate covering neural network. In
Rashid, K. M., Datta, S., & Behzadan, A. H. (2017). Coupling risk attitude and motion data mining in a preemtive construction safety framework. In
Rubaiyat, A. H. M., Toma, T. T., Kalantari-Khandani, M., Rahman, S. A., Chen, L., Ye, Y., & Pan, C. S. (2016). Automatic detection of helmet uses for construction safety. In
Ruso, J., & Stojanović, V. (2012). Occupational health and safety using data mining.
Saâdaoui, F., Bertrand, P. R., Boudet, G., Rouffiac, K., Dutheil, F., & Chamoux, A. (2015). A dimensionally reduced clustering methodology for heterogeneous occupational medicine data mining.
Sanchez-Pi, N., Marti, L., Molina, J. M., & Garcia, A. C. B. (2014). An information fusion framework for context-based accidents prevention. In
Sanmiquel, L., Bascompta, M., Rossell, J. M., Anticoi, H. F., & Guash, E. (2018). Analysis of occupational accidents in underground and surface mining in Spain using data-mining techniques.
Sanmiquel, L., Rossell, J. M., & Vintró, C. (2015). Study of Spanish mining accidents using data mining techniques.
Sanni-Anibire, M. O., Mahmoud, A. S., Hassanain, M. A., & Salami, B. A. (2020). A risk assessment approach for enhancing construction safety performance.
Sarkar, S., Lodhi, V., & Maiti, J. (2019a). Text-clustering based deep neural network for prediction of occupational accident risk: a case study. In
Sarkar, S., Pateshwari, V., & Maiti, J. (2017). Predictive model for incident occurrences in steel plant in India. In
Sarkar, S., Raj, R., Vinay, S., Maiti, J., & Pratihar, D. K. (2019b). An optimization-based decision tree approach for predicting slip-trip-fall accidents at work.
Sarkar, S., Verma, A., & Maiti, J. (2018). Prediction of occupational incidents using proactive and reactive data: a data mining approach. In J. Maiti & P. K. Ray (Eds.),
Sarkar, S., Vinay, S., & Maiti, J. (2016). Text mining based safety risk assessment and prediction of occupational accidents in a steel plant. In
Sarkar, S., Vinay, S., Raj, R., Maiti, J., & Mitra, P. (2019c). Application of optimized machine learning techniques for prediction of occupational accidents.
Shein, M. M., Hamilton-Wright, A., Black, N., Samson, M., & Lecanelier, M. (2015). Assessing ergonomic and postural data for pain and fatigue markers using machine learning techniques. In
Shin, D.-P., Park, Y.-J., Seo, J., & Lee, D.-E. (2018). Association rules mined from construction accident data.
Shirali, G. A., Noroozi, M. V., & Malehi, A. S. (2018). Predicting the outcome of occupational accidents by CART and CHAID methods at a steel factory in Iran.
Siddula, M., Dai, F., Ye, Y., & Fan, J. (2016). Classifying construction site photos for roof detection.
Taylor, J. A., Lacovara, A. V., Smith, G. S., Pandian, R., & Lehto, M. (2014). Near-miss narratives from the fire service: a Bayesian analysis.
Tixier, A. J.-P., Hallowell, M. R., Rajagopalan, B., & Bowman, D. (2017). Construction safety clash detection: identifying safety incompatibilities among fundamental attributes using data mining.
Tomiazzi, J. S., Judai, M. A., Nai, G. A., Pereira, D. R., Antunes, P. A., & Favareto, A. P. A. (2018). Evaluation of genotoxic effects in Brazilian agricultural workers exposed to pesticides and cigarette smoke using machine-learning algorithms.
Tomiazzi, J. S., Pereira, D. R., Judai, M. A., Antunes, P. A., & Favareto, A. P. A. (2019). Performance of machine-learning algorithms to pattern recognition and classification of hearing impairment in Brazilian farmers exposed to pesticide and/or cigarette smoke. Environ.
Ueno, K., Hayashi, T., Iwata, K., Honda, N., Kitahara, Y., & Paul, T. K. (2008). Prioritizing health promotion plans with k-bayesian network classifier. In
Valêncio, C. R., Ichiba, F. T., Medeiros, C. A., & Souza, R. C. G. (2011). Spatial clustering applied to health area. In
Waghmare, K., & Pai, A. R. (2013). Analytical study using data mining for periodical medical examination of employees. In
Xie, X., & Chang, Z. (2018). Intelligent wearable occupational health safety assurance system of power operation.
Yanar, B., Lay, M., & Smith, P. M. (2019). The interplay between supervisor safety support and occupational health and safety vulnerability on work injury.
Yoon, S. J., Lin, H. K., Chen, G., Yi, S., Choi, J., & Rui, Z. (2013). Effect of occupational health and safety management system on work-related accident rate and differences of occupational health and safety management system awareness between managers in South Korea’s construction industry.
Zhao, Y., Li, J., Zhang, M., Lu, Y., Xie, H., Tian, Y., & Qiu, W. (2019). Machine learning models for the hearing impairment prediction in workers exposed to complex industrial noise.
Submitted date:
05/28/2021
Accepted date:
09/21/2021
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