Abstract
Retrospective analysis of the data of hospitalized coal worker patients suffering from pneumoconiosis in the Tangshan money home since 2005. Using these data which provide seniority of exposure to dust, time of exposure to dust, age and the dust class as the influence factors of length of service, which build the model of the BP neural network, and forecast the length of service of coal worker’s pneumoconiosis. Making use of SPSS statistical software package, using matching t test comparison before and after the length of service of the forecast, prediction the D-value of the length of service before and after is 0.095 ± 2.399, t = 1.225, P = 0.221. The importance of each variable distribution result shows that the importance of the biggest is seniority of exposure in dust to forecast coal worker’s pneumoconiosis (0.632), followed is the dust class (0.247), time of exposure in dust (0.061), and age (0.060). BP neural network model has high forecasting accuracy to forecast the length of service of coal worker’s pneumoconiosis.
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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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Wang, X., Wu, J., Yin, S., Wang, G., Guo, Z. (2013). Forecasting Incidence Age of Coal Workers’ Pneumoconiosis Based on BP Neural Networks. In: Yang, Y., Ma, M. (eds) Proceedings of the 2nd International Conference on Green Communications and Networks 2012 (GCN 2012): Volume 1. Lecture Notes in Electrical Engineering, vol 223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35419-9_77
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DOI: https://doi.org/10.1007/978-3-642-35419-9_77
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