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This study developed and externally validated an interpretable machine learning (ML) model to stratify the severity of periodontitis (Stage III-IV vs. Stage I-II) using routine blood biomarkers combined with demographic and behavioral factors. The stacking model showed moderate discriminatory performance with an external validation area under the curve (AUC) of 0.774. Incorporation of blood-derived markers modestly improved prediction beyond age, sex, and smoking status. However, calibration was suboptimal on external data, suggesting caution before clinical implementation. Exploratory analyses identified potential age- and sex-related variations in predictor effects, underscoring the need for further validation in diverse populations.
Study Background and Disease Burden
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