Two-centers machine learning analysis for predicting acid-fast bacilli results in tuberculosis sputum tests
Background: Tuberculosis (TB) is a chronic respiratory infectious disease caused by Mycobacterium tuberculosis, typically diagnosed through sputum smear microscopy for acid-fast bacilli (AFB) to assess the infectivity of TB. Methods: This study enrolled 769 patients, including 641 patients from the...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Journal of Clinical Tuberculosis and Other Mycobacterial Diseases |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405579425000026 |
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| author | Jichong Zhu Yong Zhao Chengqian Huang Chenxing Zhou Shaofeng Wu Tianyou Chen Xinli Zhan |
| author_facet | Jichong Zhu Yong Zhao Chengqian Huang Chenxing Zhou Shaofeng Wu Tianyou Chen Xinli Zhan |
| author_sort | Jichong Zhu |
| collection | DOAJ |
| description | Background: Tuberculosis (TB) is a chronic respiratory infectious disease caused by Mycobacterium tuberculosis, typically diagnosed through sputum smear microscopy for acid-fast bacilli (AFB) to assess the infectivity of TB. Methods: This study enrolled 769 patients, including 641 patients from the First Affiliated Hospital of Guangxi Medical University as the training group, and 128 patients from Guangxi Hospital of the First Affiliated Hospital of Sun Yat-sen University as the validation group. Among the training cohort, 107 patients were AFB-positive, and 534 were AFB-negative. In the validation cohort, 24 were AFB-positive, and 104 were AFB-negative. Blood samples were collected and analyzed using machine learning (ML) methods to identify key factors for TB diagnosis. Results: Several ML methods were compared, and support vector machine recursive feature elimination (SVM-RFE) was selected to construct a nomogram diagnostic model. The area under the curve (AUC) of the diagnostic model was 0.721 in the training cohort and 0.758 in the validation cohort. The model demonstrated clinical utility when the threshold was between 38% and 94%, with the NONE line above the ALL line in the decision curve analysis. Conclusion: We developed a diagnostic model using multiple ML methods to predict AFB results, achieving satisfactory diagnostic performance. |
| format | Article |
| id | doaj-art-e950143561184eb3b16f987dca843881 |
| institution | Kabale University |
| issn | 2405-5794 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Clinical Tuberculosis and Other Mycobacterial Diseases |
| spelling | doaj-art-e950143561184eb3b16f987dca8438812025-01-29T05:01:28ZengElsevierJournal of Clinical Tuberculosis and Other Mycobacterial Diseases2405-57942025-02-0138100511Two-centers machine learning analysis for predicting acid-fast bacilli results in tuberculosis sputum testsJichong Zhu0Yong Zhao1Chengqian Huang2Chenxing Zhou3Shaofeng Wu4Tianyou Chen5Xinli Zhan6People’s Hospital of Guilin, Guilin 541002, PR China; First Affiliated Hospital of Guangxi Medical University, Nanning 530021, PR ChinaGuangxi Hospital, the First Affiliated Hospital of Sun Yat-sen University, Nanning 530021, PR ChinaFirst Affiliated Hospital of Guangxi Medical University, Nanning 530021, PR ChinaFirst Affiliated Hospital of Guangxi Medical University, Nanning 530021, PR ChinaFirst Affiliated Hospital of Guangxi Medical University, Nanning 530021, PR ChinaFirst Affiliated Hospital of Guangxi Medical University, Nanning 530021, PR ChinaFirst Affiliated Hospital of Guangxi Medical University, Nanning 530021, PR China; Corresponding author.Background: Tuberculosis (TB) is a chronic respiratory infectious disease caused by Mycobacterium tuberculosis, typically diagnosed through sputum smear microscopy for acid-fast bacilli (AFB) to assess the infectivity of TB. Methods: This study enrolled 769 patients, including 641 patients from the First Affiliated Hospital of Guangxi Medical University as the training group, and 128 patients from Guangxi Hospital of the First Affiliated Hospital of Sun Yat-sen University as the validation group. Among the training cohort, 107 patients were AFB-positive, and 534 were AFB-negative. In the validation cohort, 24 were AFB-positive, and 104 were AFB-negative. Blood samples were collected and analyzed using machine learning (ML) methods to identify key factors for TB diagnosis. Results: Several ML methods were compared, and support vector machine recursive feature elimination (SVM-RFE) was selected to construct a nomogram diagnostic model. The area under the curve (AUC) of the diagnostic model was 0.721 in the training cohort and 0.758 in the validation cohort. The model demonstrated clinical utility when the threshold was between 38% and 94%, with the NONE line above the ALL line in the decision curve analysis. Conclusion: We developed a diagnostic model using multiple ML methods to predict AFB results, achieving satisfactory diagnostic performance.http://www.sciencedirect.com/science/article/pii/S2405579425000026Machine learningTuberculosisAcid-fast bacilliBig dataSVM-RFE |
| spellingShingle | Jichong Zhu Yong Zhao Chengqian Huang Chenxing Zhou Shaofeng Wu Tianyou Chen Xinli Zhan Two-centers machine learning analysis for predicting acid-fast bacilli results in tuberculosis sputum tests Journal of Clinical Tuberculosis and Other Mycobacterial Diseases Machine learning Tuberculosis Acid-fast bacilli Big data SVM-RFE |
| title | Two-centers machine learning analysis for predicting acid-fast bacilli results in tuberculosis sputum tests |
| title_full | Two-centers machine learning analysis for predicting acid-fast bacilli results in tuberculosis sputum tests |
| title_fullStr | Two-centers machine learning analysis for predicting acid-fast bacilli results in tuberculosis sputum tests |
| title_full_unstemmed | Two-centers machine learning analysis for predicting acid-fast bacilli results in tuberculosis sputum tests |
| title_short | Two-centers machine learning analysis for predicting acid-fast bacilli results in tuberculosis sputum tests |
| title_sort | two centers machine learning analysis for predicting acid fast bacilli results in tuberculosis sputum tests |
| topic | Machine learning Tuberculosis Acid-fast bacilli Big data SVM-RFE |
| url | http://www.sciencedirect.com/science/article/pii/S2405579425000026 |
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