Abstract
BACKGROUND: Latent tuberculosis infection (LTBI) is a significant reservoir for active tuberculosis development. Identifying key risk factors is crucial for prevention strategies. Machine learning techniques can uncover complex relationships between risk factors and disease outcomes. METHODS: Data were collected from China's Tuberculosis Management Information System. LTBI was defined by positive tuberculin skin tests. A case-control design comparing LTBI (n = 669) with active tuberculosis (ATB, n = 669) patients was employed. Propensity score matching (1:1) was performed using age, gender, and education level. Four machine learning models (random forest, XGBoost, support vector machine, and neural network) were developed for feature importance analysis. Least Absolute Shrinkage and Selection Operator (LASSO) regression and logistic regression identified key risk factors. Bootstrap resampling (n = 1,000 iterations) assessed model stability with 95% confidence intervals. Shapley Additive Explanations (SHAP) analysis provided feature importance interpretation. A risk nomogram was constructed and evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis. RESULTS: Among 1,338 matched participants, XGBoost demonstrated superior performance (AUC = 0.898, accuracy = 85.7%, sensitivity = 84.2%, specificity = 86.9%). SHAP analysis revealed age group (mean |SHAP value|=0.818) as the most influential predictor, followed by medical insurance type (0.599), income group (0.523), and education level (0.439). Logistic regression identified 11 significant risk factors: age (OR = 2.35, 95%CI: 1.86-2.96), BMI (OR = 0.81, 95%CI: 0.71-0.93), smoking status, occupational dust exposure, diabetes, medical insurance type, immunosuppressant use, education level, silicosis, anemia, and TB contact history. The nomogram showed good discrimination (AUC = 0.839) and clinical utility, identifying 64.44% of subjects as high-risk with 53.62% confirmed as true positives at 20% risk threshold. CONCLUSION: 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.