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Background <p>Tuberculosis (TB) is a major global health threat, causing 10.6 million new cases and 1.3 million deaths in 2022. Early diagnosis is crucial, but current methods are often costly and slow for resource-limited settings. This study aimed to develop a rapid, <i>low</i>-cost diagnostic tool using routine blood indicators.</p> Methods <p>We retrospectively analyzed data from 728 TB patients and 2,718 healthy controls. The training set was balanced using the ROSE technique. We trained seven machine learning models, using LASSO regression and forward selection to identify optimal features. The final model was interpreted with SHAP and deployed as an interactive Shiny application.</p> Results <p>The Gradient Boosting Machine (GBM) model performed optimally on the test set (AUC = 0.831, specificity = 0.855, sensitivity = 0.644). SHAP analysis identified platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and platelet distribution width (PDW) as key predictors. Lowering the classification threshold to 0.24 increased sensitivity to 83.6% (specificity 59.9%), demonstrating its screening potential. An interactive web application was developed to enhance clinical utility.</p> Conclusion <p>This study delivers a validated GBM model using routine blood tests as a cost-effective TB screening tool. Its high specificity can reduce unnecessary follow-up tests. The model’s core predictors are interpretable and provide biological insights into the inflammatory response in TB. The accompanying Shiny app increases accessibility, making it a promising tool for resource-limited settings.</p> Recommendations <p>We propose a phased diagnostic strategy: use this model with a low threshold (0.24) for high-sensitivity initial screening, followed by confirmatory molecular testing for positive cases. This approach balances high detection rates with resource optimization. Future work should include prospective validation with cohorts including other respiratory diseases.</p>

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Development of a machine learning model for early pulmonary tuberculosis diagnosis using blood test biomarkers

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月5日

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  • Liangqiong Chen,
  • Cuiqi Yang,
  • Yefeng Dong,
  • Renmei Ge,
  • Jianhao Xu,
  • Rongman Xu,
  • Haitao Zhang,
  • Deping Dong,
  • Feiyue Ji,
  • Jiyang Lu,
  • Jinliang Chen,
  • Yongwei Qin

摘要

Background

Tuberculosis (TB) is a major global health threat, causing 10.6 million new cases and 1.3 million deaths in 2022. Early diagnosis is crucial, but current methods are often costly and slow for resource-limited settings. This study aimed to develop a rapid, low-cost diagnostic tool using routine blood indicators.

Methods

We retrospectively analyzed data from 728 TB patients and 2,718 healthy controls. The training set was balanced using the ROSE technique. We trained seven machine learning models, using LASSO regression and forward selection to identify optimal features. The final model was interpreted with SHAP and deployed as an interactive Shiny application.

Results

The Gradient Boosting Machine (GBM) model performed optimally on the test set (AUC = 0.831, specificity = 0.855, sensitivity = 0.644). SHAP analysis identified platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and platelet distribution width (PDW) as key predictors. Lowering the classification threshold to 0.24 increased sensitivity to 83.6% (specificity 59.9%), demonstrating its screening potential. An interactive web application was developed to enhance clinical utility.

Conclusion

This study delivers a validated GBM model using routine blood tests as a cost-effective TB screening tool. Its high specificity can reduce unnecessary follow-up tests. The model’s core predictors are interpretable and provide biological insights into the inflammatory response in TB. The accompanying Shiny app increases accessibility, making it a promising tool for resource-limited settings.

Recommendations

We propose a phased diagnostic strategy: use this model with a low threshold (0.24) for high-sensitivity initial screening, followed by confirmatory molecular testing for positive cases. This approach balances high detection rates with resource optimization. Future work should include prospective validation with cohorts including other respiratory diseases.