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
• ISSN: 1660-4601; 1661-7827; 1660-4601
• DOI: 10.3390/ijerph192114300

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 analysis was used to identify the influencing factors of abnormal liver function. A restricted cubic spline model was used to further explore the influence of the length of service. Finally, a deep neural network-based model for predicting the risk of abnormal liver function among workers was developed. Of all 6087 study subjects, a total of 1018 (16.7%) cases were detected with abnormal liver function. Increased BMI, length of service, DBP, SBP, and being male were independent risk factors for abnormal liver function. The risk of abnormal liver function rises sharply with increasing length of service below 10 years. AUC values of the model were 0.764 (95% CI: 0.746–0.783) and 0.756 (95% CI: 0.727–0.786) in the training and test sets, respectively. The other four evaluation indices of the DNN model also achieved good values.