Abstract
Applying the value of BP neural network model is discussed in the occupational prediction in order to provide evidence for pneumoconiosis prevention of dust operators. The data of patients who have been diagnosed as coal workers’ pneumoconiosis were collected, and then the selected cases samples were randomly divided into three parts by the ratio of 3:1:1 to establish the BP neural network model, the fitting results of test and the forecast accuracy of the model, respectively. There was no significant difference between the model predictions and true value (P = 0.785 > 0.05), and the coefficient of determination between the true value and predictive value of validation sample and stimulation sample were 0.875 and 0.859, respectively. The predicted relative error of validation sample and stimulation sample was 12.8 % and 14.8 %, respectively, both less than 20 %. The model is good to be used in analysis that predicts incidence seniority of the health of coal workers, and the predictions were reliable and were worth to be widely applied.
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Acknowledgments
This work is supported by Hebei Science and Technology Funds (11276911D) and program of Tangshan Science and Technology Research and Development (11150205A-3).
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Wu, J., Wang, X., Guo, X., Wang, G., Su, Y., Zhou, L. (2013). Forecasting Incidence Seniority of Coal Workers’ Pneumoconiosis Based on BP Neural Network . In: Yang, Y., Ma, M. (eds) Proceedings of the 2nd International Conference on Green Communications and Networks 2012 (GCN 2012): Volume 4. Lecture Notes in Electrical Engineering, vol 226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35440-3_73
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DOI: https://doi.org/10.1007/978-3-642-35440-3_73
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