Development and validation of a machine learning-based nomogram for predicting HLA-B27 expression
Abstract Background HLA-B27 positivity is normal in patients undergoing rheumatic diseases. The diagnosis of many diseases requires an HLA-B27 examination. Methods This study screened totally 1503 patients who underwent HLA-B27 examination, liver/kidney function tests, and complete blood routine exa...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-09-01
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| Series: | BMC Immunology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12865-023-00566-z |
| _version_ | 1797577134462468096 |
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| author | Jichong Zhu Weiming Tan Xinli Zhan Qing Lu Tuo Liang JieJiang Hao Li Chenxing Zhou Shaofeng Wu Tianyou Chen Yuanlin Yao Shian Liao Chaojie Yu Liyi Chen Chong Liu |
| author_facet | Jichong Zhu Weiming Tan Xinli Zhan Qing Lu Tuo Liang JieJiang Hao Li Chenxing Zhou Shaofeng Wu Tianyou Chen Yuanlin Yao Shian Liao Chaojie Yu Liyi Chen Chong Liu |
| author_sort | Jichong Zhu |
| collection | DOAJ |
| description | Abstract Background HLA-B27 positivity is normal in patients undergoing rheumatic diseases. The diagnosis of many diseases requires an HLA-B27 examination. Methods This study screened totally 1503 patients who underwent HLA-B27 examination, liver/kidney function tests, and complete blood routine examination in First Affiliated Hospital of Guangxi Medical University. The training cohort included 509 cases with HLA-B27 positivity whereas 611 with HLA-B27 negativity. In addition, validation cohort included 147 cases with HLA-B27 positivity whereas 236 with HLA-B27 negativity. In this study, 3 ML approaches, namely, LASSO, support vector machine (SVM) recursive feature elimination and random forest, were adopted for screening feature variables. Subsequently, to acquire the prediction model, the intersection was selected. Finally, differences among 148 cases with HLA-B27 positivity and negativity suffering from ankylosing spondylitis (AS) were investigated. Results Six factors, namely red blood cell count, human major compatibility complex, mean platelet volume, albumin/globulin ratio (ALB/GLB), prealbumin, and bicarbonate radical, were chosen with the aim of constructing the diagnostic nomogram using ML methods. For training queue, nomogram curve exhibited the value of area under the curve (AUC) of 0.8254496, and C-value of the model was 0.825. Moreover, nomogram C-value of the validation queue was 0.853, and the AUC value was 0.852675. Furthermore, a significant decrease in the ALB/GLB was noted among cases with HLA-B27 positivity and AS cases. Conclusion To conclude, the proposed ML model can effectively predict HLA-B27 and help doctors in the diagnosis of various immune diseases. |
| first_indexed | 2024-03-10T22:03:41Z |
| format | Article |
| id | doaj.art-cfbbefc29f434643bbc1e7bef823dce6 |
| institution | Directory Open Access Journal |
| issn | 1471-2172 |
| language | English |
| last_indexed | 2024-03-10T22:03:41Z |
| publishDate | 2023-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Immunology |
| spelling | doaj.art-cfbbefc29f434643bbc1e7bef823dce62023-11-19T12:52:13ZengBMCBMC Immunology1471-21722023-09-0124111510.1186/s12865-023-00566-zDevelopment and validation of a machine learning-based nomogram for predicting HLA-B27 expressionJichong Zhu0Weiming Tan1Xinli Zhan2Qing Lu3Tuo Liang4JieJiang5Hao Li6Chenxing Zhou7Shaofeng Wu8Tianyou Chen9Yuanlin Yao10Shian Liao11Chaojie Yu12Liyi Chen13Chong Liu14The First Affiliated Hospital of Guangxi Medical UniversityThe First Affiliated Hospital of Guangxi Medical UniversityThe First Affiliated Hospital of Guangxi Medical UniversityThe First Affiliated Hospital of Guangxi Medical UniversityThe First Affiliated Hospital of Guangxi Medical UniversityThe First Affiliated Hospital of Guangxi Medical UniversityThe First Affiliated Hospital of Guangxi Medical UniversityThe First Affiliated Hospital of Guangxi Medical UniversityThe First Affiliated Hospital of Guangxi Medical UniversityThe First Affiliated Hospital of Guangxi Medical UniversityThe First Affiliated Hospital of Guangxi Medical UniversityThe First Affiliated Hospital of Guangxi Medical UniversityThe First Affiliated Hospital of Guangxi Medical UniversityThe First Affiliated Hospital of Guangxi Medical UniversityThe First Affiliated Hospital of Guangxi Medical UniversityAbstract Background HLA-B27 positivity is normal in patients undergoing rheumatic diseases. The diagnosis of many diseases requires an HLA-B27 examination. Methods This study screened totally 1503 patients who underwent HLA-B27 examination, liver/kidney function tests, and complete blood routine examination in First Affiliated Hospital of Guangxi Medical University. The training cohort included 509 cases with HLA-B27 positivity whereas 611 with HLA-B27 negativity. In addition, validation cohort included 147 cases with HLA-B27 positivity whereas 236 with HLA-B27 negativity. In this study, 3 ML approaches, namely, LASSO, support vector machine (SVM) recursive feature elimination and random forest, were adopted for screening feature variables. Subsequently, to acquire the prediction model, the intersection was selected. Finally, differences among 148 cases with HLA-B27 positivity and negativity suffering from ankylosing spondylitis (AS) were investigated. Results Six factors, namely red blood cell count, human major compatibility complex, mean platelet volume, albumin/globulin ratio (ALB/GLB), prealbumin, and bicarbonate radical, were chosen with the aim of constructing the diagnostic nomogram using ML methods. For training queue, nomogram curve exhibited the value of area under the curve (AUC) of 0.8254496, and C-value of the model was 0.825. Moreover, nomogram C-value of the validation queue was 0.853, and the AUC value was 0.852675. Furthermore, a significant decrease in the ALB/GLB was noted among cases with HLA-B27 positivity and AS cases. Conclusion To conclude, the proposed ML model can effectively predict HLA-B27 and help doctors in the diagnosis of various immune diseases.https://doi.org/10.1186/s12865-023-00566-zHLA-B27Machine learning algorithmsPrediction modelNomogramImmunological diseases |
| spellingShingle | Jichong Zhu Weiming Tan Xinli Zhan Qing Lu Tuo Liang JieJiang Hao Li Chenxing Zhou Shaofeng Wu Tianyou Chen Yuanlin Yao Shian Liao Chaojie Yu Liyi Chen Chong Liu Development and validation of a machine learning-based nomogram for predicting HLA-B27 expression BMC Immunology HLA-B27 Machine learning algorithms Prediction model Nomogram Immunological diseases |
| title | Development and validation of a machine learning-based nomogram for predicting HLA-B27 expression |
| title_full | Development and validation of a machine learning-based nomogram for predicting HLA-B27 expression |
| title_fullStr | Development and validation of a machine learning-based nomogram for predicting HLA-B27 expression |
| title_full_unstemmed | Development and validation of a machine learning-based nomogram for predicting HLA-B27 expression |
| title_short | Development and validation of a machine learning-based nomogram for predicting HLA-B27 expression |
| title_sort | development and validation of a machine learning based nomogram for predicting hla b27 expression |
| topic | HLA-B27 Machine learning algorithms Prediction model Nomogram Immunological diseases |
| url | https://doi.org/10.1186/s12865-023-00566-z |
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