Development of a multi-laboratory integrated predictive model for silicosis utilizing machine learning: a retrospective case-control study
ObjectiveDue 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.MethodsA case-control study was conducted to screen biomarkers for...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1450439/full |
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| author | Guo-kang Sun Yun-hui Xiang Lu Wang Pin-pin Xiang Zi-xin Wang Jing Zhang Ling Wu |
| author_facet | Guo-kang Sun Yun-hui Xiang Lu Wang Pin-pin Xiang Zi-xin Wang Jing Zhang Ling Wu |
| author_sort | Guo-kang Sun |
| collection | DOAJ |
| description | ObjectiveDue 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.MethodsA 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.ResultsThe 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.ConclusionA 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. |
| format | Article |
| id | doaj-art-a6bb23be158443b1a5e11dae27dae94b |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-a6bb23be158443b1a5e11dae27dae94b2025-01-15T06:10:39ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-01-011210.3389/fpubh.2024.14504391450439Development of a multi-laboratory integrated predictive model for silicosis utilizing machine learning: a retrospective case-control studyGuo-kang Sun0Yun-hui Xiang1Lu Wang2Pin-pin Xiang3Zi-xin Wang4Jing Zhang5Ling Wu6Department of Laboratory, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, ChinaSichuan International Travel Health Care Center (Chengdu Customs Port Outpatient Department), Chengdu, ChinaDepartment of Laboratory, Akesu Center of Disease Control and Prevention, Akesu, ChinaDepartment of Laboratory, Xiping Community Healthcare Center of Longquanyi District, Chengdu, ChinaDepartment of Laboratory, Wangjiang Hospital, Sichuan University, Chengdu, ChinaDepartment of Laboratory, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, ChinaDepartment of Laboratory, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, ChinaObjectiveDue 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.MethodsA 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.ResultsThe 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.ConclusionA 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.https://www.frontiersin.org/articles/10.3389/fpubh.2024.1450439/fullsilicosisearly diagnosticsliquid biopsybiomarkersmachine learning |
| spellingShingle | Guo-kang Sun Yun-hui Xiang Lu Wang Pin-pin Xiang Zi-xin Wang Jing Zhang Ling Wu Development of a multi-laboratory integrated predictive model for silicosis utilizing machine learning: a retrospective case-control study Frontiers in Public Health silicosis early diagnostics liquid biopsy biomarkers machine learning |
| title | Development of a multi-laboratory integrated predictive model for silicosis utilizing machine learning: a retrospective case-control study |
| title_full | Development of a multi-laboratory integrated predictive model for silicosis utilizing machine learning: a retrospective case-control study |
| title_fullStr | Development of a multi-laboratory integrated predictive model for silicosis utilizing machine learning: a retrospective case-control study |
| title_full_unstemmed | Development of a multi-laboratory integrated predictive model for silicosis utilizing machine learning: a retrospective case-control study |
| title_short | Development of a multi-laboratory integrated predictive model for silicosis utilizing machine learning: a retrospective case-control study |
| title_sort | development of a multi laboratory integrated predictive model for silicosis utilizing machine learning a retrospective case control study |
| topic | silicosis early diagnostics liquid biopsy biomarkers machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1450439/full |
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