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April 1, 2026Infection and Drug ResistanceOpen Access

Analysis of High-Risk Factors for Tuberculosis Retreatment Based on Machine Learning and Latent Class Analysis

Xilong Du Maiwulajiang Yimamu Yan Na Xiaoxue Li Ziyu Wang Zulimire Nuermaihaimaiti Yuxin Wang Liping Zhang Yanling Zheng

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Key Points

Identify high-risk factors for tuberculosis retreatment using machine learning and latent class analysis.
Collected data from 6,821 tuberculosis patients retrospectively
Applied machine learning techniques including XGBoost and logistic regression
Conducted latent class analysis to identify patient subgroups
XGBoost achieved 84% accuracy and AUC of 0.938
Sputum examination 'not performed' had an OR of 123.47 (P < 0.001) for retreatment risk
Identified comorbid diabetes and prior treatment failure as high-risk factors in latent class analysis

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Implication

Identifies key risk factors for tuberculosis retreatment, suggesting improved management strategies.

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Cite This Study

Du et al. (2026) studied this question.

synapsesocial.com/papers/69eefcaefede9185760d3932 https://doi.org/10.2147/idr.s594300

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