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ORIGINAL RESEARCH article

Front. Public Health
Sec. Occupational Health and Safety
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1450439

Development of a multi-laboratory integrated predictive model for silicosis utilizing machine learning: a retrospective case-control study

Provisionally accepted
Guokang Sun Guokang Sun 1Yunhui Xiang Yunhui Xiang 2Lu Wang Lu Wang 3*Pinpin Xiang Pinpin Xiang 4Zi-Xin Wang Zi-Xin Wang 5*Jing Zhang Jing Zhang 1*Ling Wu Ling Wu 1*
  • 1 Department of Laboratory, West China School of Public Health and West China Fourth Hospital, Sichuan University, chengdu, China
  • 2 Sichuan International Travel & Health Care Center, Chengdu, Sichuan Province, China
  • 3 Akesu Center of Disease Control and Prevention, Akesu, China
  • 4 Xiping Community Healthcare Center of Longquanyi District, chengdu, China
  • 5 Wangjiang Hospital, Sichuan University, chengdu, China

The final, formatted version of the article will be published soon.

    Objective: Due to the high global prevalence of silicosis and the ongoing challenges in its diagnosis, this pilot study aims to screen biomarkers from routine blood parameters and develop a multi-biomarker model for its early detection.A case-control study was conducted to screen biomarkers for the diagnosis of silicosis using LASSO regression, SVM and RF. A sample of 612 subjects (half cases and half controls) were randomly divided into training and test groups in a 2:1 ratio. Logistic regression analysis and receiver operating characteristic (ROC) curves were used to construct a multiple biomarker-based model for the diagnosis of silicosis, which was applied to both the training and the testing datasets.The training cohort revealed significant statistical differences (P < 0.05) in multiple hematologic parameters between silicosis patients and healthy individuals. Based on machine learning, eight silicosis biomarkers were screened and identified from routine blood cell, biochemical and coagulation parameters. D-dimer (DD), Albumin/Globulin (A/G), lactate dehydrogenase (LDH) and white blood cells (WBC) were selected for constructing the logistic regression model for silicosis diagnostics. This model had a satisfactory performance in the training cohort with an area under the ROC curve (AUC) of 0.982, a diagnostic sensitivity of 95.4%, and a specificity of 92.2%. In addition, the model had a prediction accuracy of 0.936 with an AUC of 0.979 in the independent test cohort. Moreover, the diagnostic accuracies of the logistic model in silicosis stages 1, 2, and 3 were 88.0%, 95.4%, and 94.3% with an AUC of 0.968, 0.983, and 0.990 for silicosis, respectively.A diagnostic model based on DD, A/G, LDH and WBC is successfully proposed for silicosis diagnostics. It is cheap, sensitive, specific, and preliminarily offers a potential strategy for the large-scale screening of silicosis.

    Keywords: Silicosis, early diagnostics, liquid biopsy, biomarkers, machine learning

    Received: 17 Jun 2024; Accepted: 23 Dec 2024.

    Copyright: © 2024 Sun, Xiang, Wang, Xiang, Wang, Zhang and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Lu Wang, Akesu Center of Disease Control and Prevention, Akesu, China
    Zi-Xin Wang, Wangjiang Hospital, Sichuan University, chengdu, China
    Jing Zhang, Department of Laboratory, West China School of Public Health and West China Fourth Hospital, Sichuan University, chengdu, China
    Ling Wu, Department of Laboratory, West China School of Public Health and West China Fourth Hospital, Sichuan University, chengdu, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.