Prediction of tuberculosis treatment outcomes using biochemical makers with machine learning
Abstract Background Tuberculosis (TB) continues to pose a significant threat to global public health. Enhancing patient prognosis is essential for alleviating the disease burden. Objective This study aims to evaluate TB prognosis by incorporating treatment discontinuation into the assessment framewo...
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| Format: | Article |
| Language: | English |
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BMC
2025-02-01
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| Series: | BMC Infectious Diseases |
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| Online Access: | https://doi.org/10.1186/s12879-025-10609-y |
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| author | Zheyue Wang Zhenpeng Guo Weijia Wang Qiang Zhang Suya Song Yuan Xue Zhixin Zhang Jianming Wang |
| author_facet | Zheyue Wang Zhenpeng Guo Weijia Wang Qiang Zhang Suya Song Yuan Xue Zhixin Zhang Jianming Wang |
| author_sort | Zheyue Wang |
| collection | DOAJ |
| description | Abstract Background Tuberculosis (TB) continues to pose a significant threat to global public health. Enhancing patient prognosis is essential for alleviating the disease burden. Objective This study aims to evaluate TB prognosis by incorporating treatment discontinuation into the assessment framework, expanding beyond mortality and drug resistance. Methods Seven feature selection methods and twelve machine learning algorithms were utilized to analyze admission test data from TB patients, identifying predictive features and building prognostic models. SHapley Additive exPlanations (SHAP) were applied to evaluate feature importance in top-performing models. Results Analysis of 1,086 TB cases showed that a K-Nearest Neighbor classifier with Mutual Information feature selection achieved an area under the receiver operation curve (AUC) of 0.87 (95% CI: 0.83–0.92). Key predictors of treatment failure included elevated levels of 5’-nucleotidase, uric acid, globulin, creatinine, cystatin C, and aspartate transaminase. SHAP analysis highlighted 5’-nucleotidase, uric acid, and globulin as having the most significant influence on predicting treatment discontinuation. Conclusion Our model provides valuable insights into TB outcomes based on initial patient tests, potentially guiding prevention and control strategies. Elevated biomarker levels before therapy are associated with increased risk of treatment discontinuation, indicating their potential as early warning indicators. |
| format | Article |
| id | doaj-art-7d665d6092d54fd79614cb51ca60e5d4 |
| institution | DOAJ |
| issn | 1471-2334 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Infectious Diseases |
| spelling | doaj-art-7d665d6092d54fd79614cb51ca60e5d42025-08-20T03:13:14ZengBMCBMC Infectious Diseases1471-23342025-02-012511910.1186/s12879-025-10609-yPrediction of tuberculosis treatment outcomes using biochemical makers with machine learningZheyue Wang0Zhenpeng Guo1Weijia Wang2Qiang Zhang3Suya Song4Yuan Xue5Zhixin Zhang6Jianming Wang7Department of Epidemiology, Center for Global Health, School of Public Health, National Vaccine Innovation Platform, Nanjing Medical UniversityDepartment of Epidemiology, Center for Global Health, School of Public Health, National Vaccine Innovation Platform, Nanjing Medical UniversitySchool of Information and Software, University of Electronic Science and Technology of ChinaDepartment of Epidemiology, Center for Global Health, School of Public Health, National Vaccine Innovation Platform, Nanjing Medical UniversityChangzhou Medical Center, Nanjing Medical UniversityChangzhou Medical Center, Nanjing Medical UniversityChangzhou Medical Center, Nanjing Medical UniversityDepartment of Epidemiology, Center for Global Health, School of Public Health, National Vaccine Innovation Platform, Nanjing Medical UniversityAbstract Background Tuberculosis (TB) continues to pose a significant threat to global public health. Enhancing patient prognosis is essential for alleviating the disease burden. Objective This study aims to evaluate TB prognosis by incorporating treatment discontinuation into the assessment framework, expanding beyond mortality and drug resistance. Methods Seven feature selection methods and twelve machine learning algorithms were utilized to analyze admission test data from TB patients, identifying predictive features and building prognostic models. SHapley Additive exPlanations (SHAP) were applied to evaluate feature importance in top-performing models. Results Analysis of 1,086 TB cases showed that a K-Nearest Neighbor classifier with Mutual Information feature selection achieved an area under the receiver operation curve (AUC) of 0.87 (95% CI: 0.83–0.92). Key predictors of treatment failure included elevated levels of 5’-nucleotidase, uric acid, globulin, creatinine, cystatin C, and aspartate transaminase. SHAP analysis highlighted 5’-nucleotidase, uric acid, and globulin as having the most significant influence on predicting treatment discontinuation. Conclusion Our model provides valuable insights into TB outcomes based on initial patient tests, potentially guiding prevention and control strategies. Elevated biomarker levels before therapy are associated with increased risk of treatment discontinuation, indicating their potential as early warning indicators.https://doi.org/10.1186/s12879-025-10609-yTuberculosisMachine learningTreatment outcomesDiscontinuation |
| spellingShingle | Zheyue Wang Zhenpeng Guo Weijia Wang Qiang Zhang Suya Song Yuan Xue Zhixin Zhang Jianming Wang Prediction of tuberculosis treatment outcomes using biochemical makers with machine learning BMC Infectious Diseases Tuberculosis Machine learning Treatment outcomes Discontinuation |
| title | Prediction of tuberculosis treatment outcomes using biochemical makers with machine learning |
| title_full | Prediction of tuberculosis treatment outcomes using biochemical makers with machine learning |
| title_fullStr | Prediction of tuberculosis treatment outcomes using biochemical makers with machine learning |
| title_full_unstemmed | Prediction of tuberculosis treatment outcomes using biochemical makers with machine learning |
| title_short | Prediction of tuberculosis treatment outcomes using biochemical makers with machine learning |
| title_sort | prediction of tuberculosis treatment outcomes using biochemical makers with machine learning |
| topic | Tuberculosis Machine learning Treatment outcomes Discontinuation |
| url | https://doi.org/10.1186/s12879-025-10609-y |
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