IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i21p14300-d960433.html
   My bibliography  Save this article

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

Author

Listed:
  • Linghao Ni

    (School of Public Health, Chongqing Medical University, Chongqing 400016, China
    These authors contributed equally to this paper.)

  • Fengqiong Chen

    (Department of Occupational Health and Radiation Health, Chongqing Center for Disease Control and Prevention, Chongqing 400042, China
    These authors contributed equally to this paper.)

  • Ruihong Ran

    (Department of Occupational Health and Radiation Health, Chongqing Center for Disease Control and Prevention, Chongqing 400042, China)

  • Xiaoping Li

    (Department of Occupational Health and Radiation Health, Chongqing Center for Disease Control and Prevention, Chongqing 400042, China)

  • Nan Jin

    (Department of Occupational Health and Radiation Health, Chongqing Center for Disease Control and Prevention, Chongqing 400042, China)

  • Huadong Zhang

    (Department of Occupational Health and Radiation Health, Chongqing Center for Disease Control and Prevention, Chongqing 400042, China)

  • Bin Peng

    (School of Public Health, Chongqing Medical University, Chongqing 400016, China)

Abstract

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.

Suggested Citation

  • Linghao Ni & Fengqiong Chen & Ruihong Ran & Xiaoping Li & Nan Jin & Huadong Zhang & Bin Peng, 2022. "A Deep Learning-Based Model for Predicting Abnormal Liver Function in Workers in the Automotive Manufacturing Industry: A Cross-Sectional Survey in Chongqing, China," IJERPH, MDPI, vol. 19(21), pages 1-14, November.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:14300-:d:960433
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/21/14300/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/21/14300/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Constanța Rînjea & Oana Roxana Chivu & Doru-Costin Darabont & Anamaria Ioana Feier & Claudia Borda & Marilena Gheorghe & Dan Florin Nitoi, 2022. "Influence of the Thermal Environment on Occupational Health and Safety in Automotive Industry: A Case Study," IJERPH, MDPI, vol. 19(14), pages 1-13, July.
    2. Ana Assunção & Vera Moniz-Pereira & Carlos Fujão & Sarah Bernardes & António P. Veloso & Filomena Carnide, 2021. "Predictive Factors of Short-Term Related Musculoskeletal Pain in the Automotive Industry," IJERPH, MDPI, vol. 18(24), pages 1-12, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nafiseh Mollaei & Carlos Fujao & Luis Silva & Joao Rodrigues & Catia Cepeda & Hugo Gamboa, 2022. "Human-Centered Explainable Artificial Intelligence: Automotive Occupational Health Protection Profiles in Prevention Musculoskeletal Symptoms," IJERPH, MDPI, vol. 19(15), pages 1-27, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:14300-:d:960433. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.