Department of Occupational and Environmental Health, College of Public Health, Xinjiang Medical University, Wulumuqi, Xinjiang 830011, China.
Xinjiang Engineering Technology Research Center for Green Processing of Nature Product Center, Xinjiang Autonomous Academy of Instrumental Analysis, Urumqi, Xinjiang 830011, China.
Comput Math Methods Med. 2019 Sep 29;2019:8159506. doi: 10.1155/2019/8159506. eCollection 2019.
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.
职业病在中国是一个巨大的问题，许多工人都处于危险之中。准确预测职业病的发病率可以为预防和控制提供关键信息。因此，在这项研究中，评估了五种混合算法组合模型在预测中国职业病发病率方面的有效性和适用性。这五种混合算法组合模型是五种灰色模型（EGM、ODGM、EDGM、DGM 和 Verhulst）和五种最先进的机器学习模型（KNN、SVM、RF、GBM 和 ANN）的组合。这些模型的质量是基于模型预测的准确性以及最小化平均绝对百分比误差（MAPE）和均方根误差（RMSE）来评估的。结果表明，GM-ANN 模型在所有模型中提供了最精确的预测，平均绝对百分比误差（MAPE）最低为 3.49%，均方根误差（RMSE）为 1076.60。因此，GM-ANN 模型可用于精确预测中国的职业病，这可能为未来职业病的预防和控制提供有价值的信息。
