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

• Main Authors: Jianhui Wu, Sheng Qin, Jie Wang, Jing Li, Han Wang, Huiyuan Li, Zhe Chen, Chao Li, Jiaojiao Wang, Juxiang Yuan
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-09-01
• Series: Frontiers in Bioengineering and Biotechnology
• Subjects: deep learning; convolutional neural network; dyslipidemia; steel worker; disease model prediction; model performance comparison
• Online Access: https://www.frontiersin.org/article/10.3389/fbioe.2020.00839/full

The convolutional neural network (CNN) has made certain progress in image processing, language processing, medical information processing and other aspects, and there are few relevant researches on its application in disease risk prediction. 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susceptible to disease than the general population, which makes the risk prediction model for the general population no longer applicable to steel workers. Therefore, it is necessary to establish a new model dedicated to the prediction of dyslipidemia of steel workers. In this study, the physical examination information of thousands of steel workers was collected, and the risk factors of dyslipidemia in steel workers were screened out. Then, based on the data characteristics, the corresponding parameters were set for the convolutional neural network model, and the risk of dyslipidemia in steel workers was predicted by using convolutional neural network. Finally, the predictive performance of the convolutional neural network model is compared with the existing predictive models of dyslipidemia, logistics regression model and BP neural network model. The results show that the convolutional neural network has a good predictive performance in the risk prediction of dyslipidemia of steel workers, and is superior to the Logistic regression model and BP neural network model.