A Deep Learning-Based Model for Predicting Abnormal Liver Function in Workers in the Automotive Manufacturing Industry: A Cross-Sectional Survey in Chongqing, China

• Published in: International journal of environmental research and public health Vol. 19; no. 21; p. 14300
• Main Authors: Ni, Linghao, Chen, Fengqiong, Ran, Ruihong, Li, Xiaoping, Jin, Nan, Zhang, Huadong, Peng, Bin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.11.2022; MDPI
• Subjects: Automobile industry; Automobile production; Blood pressure; China - epidemiology; Cross-Sectional Studies; Data collection; Deep Learning; Enzymes; Female; Hearing loss; Humans; Laboratories; Liver Diseases; Machine learning; Male; Manufacturing; Manufacturing Industry; Occupational health; Population; Regulation; VOCs; Volatile organic compounds; Workers; China; Chongqing China; deep learning; risk factors; predictive model; abnormal liver function; automotive manufacturing industry

To identify the influencing factors and develop a predictive model for the risk of abnormal liver function in the automotive manufacturing industry works in Chongqing. Automotive manufacturing workers in Chongqing city surveyed during 2019–2021 were used as the study subjects. Logistic regression an...