AUTHOR=Wu Jianhui , Qin Sheng , Wang Jie , Li Jing , Wang Han , Li Huiyuan , Chen Zhe , Li Chao , Wang Jiaojiao , Yuan Juxiang 

TITLE=Develop and Evaluate a New and Effective Approach for Predicting Dyslipidemia in Steel Workers

JOURNAL=Frontiers in Bioengineering and Biotechnology

VOLUME=Volume 8 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2020.00839

DOI=10.3389/fbioe.2020.00839

ISSN=2296-4185

ABSTRACT=With the improvement of living standard, the incidence of dyslipidemia also increases gradually. Dyslipidemia is a major and modifiable risk factor for cardiovascular disease, early detection of dyslipidemia and early intervention can effectively reduce the occurrence of cardiovascular diseases. Risk prediction model can effectively identify high-risk groups and is widely used in public health and clinical medicine. Steel workers are a special occupational group. Their particular occupational hazards, such as high temperatures, noise and shift work, make them more susceptible to disease than the general population, which makes the risk prediction model for the general population no longer applicable to steel workers. In this study, a convolutional neural network was used to construct a risk prediction model for dyslipidemia in steel workers, and the prediction performance was compared with that of the logistics model and the BP neural network model. The results showed that the accuracy of the convolutional neural network prediction model is 94.72%, 90.77% and 91.84% in training set, test set and verification set, respectively. Moreover, the sensitivity, specificity and AUC of the convolutional neural network model are higher than those of the logistics regression model and the BP neural network model. Therefore, the convolution neural network model can be used to predict the early risk of dyslipidemia in steel workers, and provide the basis for formulating the early prevention strategy of dyslipidemia in steel workers.