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Comparative Study
. 2019 Sep 29:2019:8159506.
doi: 10.1155/2019/8159506. eCollection 2019.

A Comparative Study on the Prediction of Occupational Diseases in China with Hybrid Algorithm Combing Models

Yaoqin Lu  1 , Huan Yan  2 , Lijiang Zhang  3 , Jiwen Liu  1
Affiliations
Comparative Study

A Comparative Study on the Prediction of Occupational Diseases in China with Hybrid Algorithm Combing Models

Yaoqin Lu et al. Comput Math Methods Med. .

Abstract

Occupational disease is a huge problem in China, and many workers are under risk. Accurate forecasting of occupational disease incidence can provide critical information for prevention and control. Therefore, in this study, five hybrid algorithm combing models were assessed on their effectiveness and applicability to predict the incidence of occupational diseases in China. The five hybrid algorithm combing models are the combination of five grey models (EGM, ODGM, EDGM, DGM, and Verhulst) and five state-of-art machine learning models (KNN, SVM, RF, GBM, and ANN). The quality of the models were assessed based on the accuracy of model prediction as well as minimizing mean absolute percentage error (MAPE) and root-mean-squared error (RMSE). Our results showed that the GM-ANN model provided the most precise prediction among all the models with lowest mean absolute percentage error (MAPE) of 3.49% and root-mean-squared error (RMSE) of 1076.60. Therefore, the GM-ANN model can be used for precise prediction of occupational diseases in China, which may provide valuable information for the prevention and control of occupational diseases in the future.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The incidence of occupational diseases in China from 2005 to 2017. The dashed line indicates the first 2/3 of the data used as the training set, and the solid line indicates the last 1/3 of the data used as the testing set. The Y-axis represents the number of occupational diseases, and the X-axis represents the time series.
Figure 2
Figure 2
Flowchart of the hybrid method.
Figure 3
Figure 3
Comparison among real and fitted curves of different grey models for occupational diseases in China.
Figure 4
Figure 4
Comparison among real and fitted curves of GM-KNN models.
Figure 5
Figure 5
Comparison among real and fitted curves of GM-SVM models.
Figure 6
Figure 6
Comparison among real and fitted curves of hybrid models.
See this image and copyright information in PMC

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