Artificial Neural Network Modeling of Prevalence of Pneumoconiosis among Workers in Metallurgical Industry - A Case Study

The paper describes the training, validation and application of artificial neural network (ANN) models for prevalence of pneumoconiosis among workers in Yueyufeng iron and steel company (China). The models employed three input variables collected at several operational sites in 30 different iron and steel companies. The performance of the ANN models was assessed through the global error. The model achieves more satisfactory due to the computed values of prevalence of pneumoconiosis were in close agreement with their respective collected data sets. The trained ANN models can be used as tools for forecasting prevalence of pneumoconiosis among workers in metallurgical industry, and then for individual occupational disease management.