ArticleFrontiers in medicine2026
Clinical data analysis research on tuberculosis based on machine learning.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Tuberculosis (TB) remains a global health challenge, with heterogeneous treatment outcomes despite standardized protocols. Traditional statistical models struggle with high-dimensional clinical data, necessitating advanced machine learning (ML) approaches. Objective: To analyze clinical data from 467 pulmonary TB patients and construct a predictive model using multiple ML algorithms. Methods: A prospective cohort of 467 patients (218 intervention, 249 control) was enrolled from Xi'an Chest Hospital. Medical ratio features (ALT/AST, CD4/CD8) and polynomial interaction terms (e.g., RBC × ALT) were constructed. Recursive feature elimination (RFE) selected 60 predictive factors from an expanded 80-dimensional feature space. Fourteen ML algorithms were systematically compared, with hyperparameters optimized via grid search. Performance was assessed using five-fold cross-validation Results: LightGBM achieved the highest initial predictive performance ( Conclusion: The optimized Random Forest model ( Clinical trial registration: Registration Platform: Chinese Clinical Trial Registry [https://www.chictr.org.cn/], identifier [ChiCTR2300074328].
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