Machine learning identifies risk factors for latent tuberculosis infection, suggesting targeted interventions may reduce disease burden.
This study successfully identified key LTBI risk factors using machine learning approaches. The developed nomogram provides a practical tool for targeted screening in resource-limited settings. Interventions targeting modifiable factors such as smoking cessation and occupational dust control may reduce LTBI and active TB burden.
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Wang et al. (2025) studied this question.
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