Development and External Validation of An Interpretable Machine Learning Model for Severity Stratification of Current Periodontitis Using Routine Blood Biomarkers, With Exploratory Analyses of Age‐ and Sex‐Specific Model Behaviour

AIM: To develop and externally validate an interpretable machine learning model using routine laboratory data to distinguish current Stage III-IV from Stage I-II periodontitis among adults with established periodontitis. MATERIALS AND METHODS: This two-centre retrospective study included 1321 patients for development and 584 for external validation at two hospitals of the Zhejiang University School of Medicine. The stacking model used age, sex, smoking status and 13 blood-derived predictors and was frozen before external validation. Performance was assessed by discrimination, calibration, classification measures, decision-curve analysis and comparison with simpler models. SHAP and restricted cubic spline analyses were exploratory. RESULTS: The stacking model had an out-of-fold AUC of 0.827 (95% CI: 0.802-0.851) in development and an external AUC of 0.774 (95% CI: 0.730-0.818). External calibration was less favourable (intercept 0.785, slope 0.822). External AUC was 0.745 for age, sex and smoking status and 0.763 after adding the 13 blood-derived predictors. CONCLUSIONS: Among adults with established periodontitis and sufficiently complete predictor data, the model showed moderate external discrimination but less favourable calibration. Blood-derived predictors added modest discrimination beyond age, sex and smoking status. The model does not replace periodontal examination. Further multi-centre validation and calibration are needed before clinical use.