Develop and Evaluate a New and Effective Approach for Predicting Dyslipidemia in Steel Workers
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 car...
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: | |
Online Access: | https://www.frontiersin.org/article/10.3389/fbioe.2020.00839/full |
Similar Items
-
Research on Risk Prediction of Dyslipidemia in Steel Workers Based on Recurrent Neural Network and LSTM Neural Network
by: Shiyue Cui, et al.
Published: (2020-01-01) -
Job category differences in the prevalence and associated factors of insomnia in steel workers in China
by: Xiaoming Li, et al.
Published: (2020-03-01) -
An association between cumulative exposure to light at night and the prevalence of hyperuricemia in steel workers
by: Xiaoming Li, et al.
Published: (2021-06-01) -
Comprehensive Evaluation System of Occupational Hazard Prevention and Control in Iron and Steel Enterprises Based on A Modified Delphi Technique
by: Yang Song, et al.
Published: (2020-01-01) -
Prevalence and Risk Factors Associated with Dyslipidemia in Chongqing, China
by: Li Qi, et al.
Published: (2015-10-01)