Abstract
In occupational safety, when a neural network is trained, it is possible to predict the outcome given a combination of risk factors. Risk assessment is probably the most important issue in occupational safety. Risk assessment facilitates the design and prioritization of effective prevention measures. Neural network were applied for predicting the severity of accidents, which is important to assess risks. Data sets were obtained from the official accident notifications in the manufacturing sector of Andalusia in 2011. The results confirm that neural networks are useful in risk factor estimation. Association analysis was used to identify the most important risk factors within the predicting variables. Diagnostic array analyses show that for preventive purposes it is better to use a reduced data set with a case-control approach in order to improve the specificity and the sensitivity.
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Carrillo-Castrillo, J., Guadix Martín, J., Grosso de la Vega, R., Onieva, L. (2014). Neural Network Application for Risk Factors Estimation in Manufacturing Accidents. In: Hernández, C., López-Paredes, A., Pérez-Ríos, J. (eds) Managing Complexity. Lecture Notes in Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-04705-8_32
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DOI: https://doi.org/10.1007/978-3-319-04705-8_32
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