Abstract
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susceptible to disease than the general population, which makes the risk prediction model for the general population no longer applicable to steel workers.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.
Highlights
Dyslipidemia is a chronic noncommunicable disease of lipid metabolism disorder, characterized by increased and/or decreased lipid levels in the blood
Studies have shown (Miller, 2009; Hendrani et al, 2016; Stevens et al, 2016) that the early detection and management of high-risk groups with dyslipidemia can effectively reduce the incidence of cardiovascular disease, which can reduce the burden of cardiovascular disease and brings great social value
Dyslipidemia refers to the total cholesterol (TC) ≥ 6.2 mmol/L, and/or triglyceride (TG) ≥ 2.3 mmol/L, and/or low-density lipoprotein cholesterol (LDL-c) ≥ 4.1 mmol/L, and/or highdensity lipoprotein cholesterol (HDL-c) < 1.0 mmol/L
Summary
Dyslipidemia is a chronic noncommunicable disease of lipid metabolism disorder, characterized by increased and/or decreased lipid levels in the blood. Evidence demonstrates that dyslipidemia is an independent and modifiable major risk factor for cardiovascular disease, and its level can significantly increase the incidence and mortality of cardiovascular disease (Pikula et al, 2015; Lee et al, 2017). Steel workers are a special occupational group, whose occupational environment is special, such as high temperature, noise, shift system and other special occupational exposure can cause or affect the occurrence of chronic diseases (Chauhan et al, 2014; Hedén Stahl et al, 2014; Tong et al, 2017; Wu et al, 2019b). The prediction model of dyslipidemia in the general population is not suitable for steel workers. In order to improve the quality of life and health status of steel workers, it is urgent to establish a new risk prediction model of dyslipidemia in steel workers
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