Wu Jianhui, Qin Sheng, Wang Jie, Li Jing, Wang Han, Li Huiyuan, Chen Zhe, Li Chao, Wang Jiaojiao, Yuan Juxiang
School of Public Health, North China University of Science and Technology, Tangshan, China.
Hebei Province Key Laboratory of Occupational Health and Safety for Coal Industry, North China University of Science and Technology, Tangshan, China.
Front Bioeng Biotechnol. 2020 Sep 10;8:839. doi: 10.3389/fbioe.2020.00839. eCollection 2020.
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susceptible to disease than the general population, which makes the risk prediction model for the general population no longer applicable to steel workers.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.
卷积神经网络（CNN）在图像处理、语言处理、医学信息处理等方面取得了一定进展，而其在疾病风险预测中的应用相关研究较少。血脂异常是心血管疾病的主要且可改变的危险因素，早期发现血脂异常并进行早期干预可有效降低心血管疾病的发生。风险预测模型能够有效识别高危人群，在公共卫生和临床医学中被广泛应用。钢铁工人是一个特殊的职业群体。他们特殊的职业危害，如高温、噪音和轮班工作，使他们比普通人群更容易患病，这使得适用于普通人群的风险预测模型不再适用于钢铁工人。因此，有必要建立一个专门用于预测钢铁工人血脂异常的新模型。在本研究中，收集了数千名钢铁工人的体检信息，筛选出钢铁工人血脂异常的危险因素。然后，根据数据特征为卷积神经网络模型设置相应参数，利用卷积神经网络预测钢铁工人血脂异常的风险。最后，将卷积神经网络模型的预测性能与现有的血脂异常预测模型、逻辑回归模型和BP神经网络模型进行比较。结果表明，卷积神经网络在钢铁工人血脂异常风险预测中具有良好的预测性能，优于逻辑回归模型和BP神经网络模型。
