Clinical data analysis research on tuberculosis based on machine learning

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: , RMSE, and MAE. Results: of 0.1867 with comparable error metrics and enhanced clinical interpretability, serving as the final reference model. Feature engineering expanded the feature space from 33 to 80, with 60 optimal features retained. Conclusion: = 0.1867) demonstrates moderate accuracy and clinical interpretability, supporting its potential as a decision-support tool for TB treatment optimization. Pharmacist-led therapeutic drug monitoring (TDM) further enhances individualized therapy. Future work requires multi-center validation and radiomics integration to improve predictive performance in severe cases. Clinical trial registration: Registration Platform: Chinese Clinical Trial Registry [https://www.chictr.org.cn/], identifier [ChiCTR2300074328].