Application of Grey Model-Generalized Regression Neural Network Combination Model to Prediction on Incidence of Pneumoconiosis in China

Objective To apply gray model plus generalized regression neural network GM(1, 1)-GRNN combination model to the prediction on incidence of pneumoconiosis in China and compare the predictive effects among GM(1, 1)-GRNN combination model, grey model (GM), and back-propagation network (BPNN).

Methods The data of pneumoconiosis incidence from 2003 to 2012 in China were collected, SAS9.3 was used to construct GM(1, 1) model, and Matlab 8.0 was used to establish BPNN model and GM(1, 1)-GRNN combination model. Afterwards, the data in 2013 were used to evaluate the predictive effects.

Results The mean relative error (MRE), mean error rate (MER), mean square error (MSE), and mean absolute error (NAE) fitted and forecasted by GM(1, 1) model were 12.041%, 0.122, 4 999 319.100, and 1 781.100 (fitted), and 20.033%, 0.200, 2 151 104.000, and 4 638.000 (forecasted); by BRNN, 9.891%, 0.124, 3 615 099.600, and 1 802.000 (fitted), and 6.932%, 0.069, 2 576 025.000, and 1 605.000 (forecasted); by GM(1, 1)-GRNN combination model, 6.490%, 0.069, 1 900 198.400, and 1 001.200 (fitted), and 3.939%, 0.039, 831 744.000, and 912.000 (forecasted), respectively. The incidences of pneumoconiosis from 2014 to 2015 forecasted by GM(1, 1)-GRNN combination model were 23 768 and 23 434, respectively.

Conclusion GM(1, 1)-GRNN combination model is superior to GM(1, 1) model and BPNN model with better fitting and predictive effects.