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Forecasting Incidence Age of Coal Workers’ Pneumoconiosis Based on BP Neural Networks

  • Xiaohong Wang
  • Jianhui Wu
  • Sufeng Yin
  • Guoli Wang
  • Zhengjun Guo
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 223)

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.

Keywords

Coal workers’ pneumoconiosis BP neural network Incidence seniority Prediction 

Notes

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xiaohong Wang
    • 1
  • Jianhui Wu
    • 2
  • Sufeng Yin
    • 2
  • Guoli Wang
    • 2
  • Zhengjun Guo
    • 2
  1. 1.Tangshan Centers for Disease Control and PreventionTangshanChina
  2. 2.Hebei United UniversityTang ShanChina

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