Myocardial infarction (MI) is a major global health concern influenced by diverse risk factors. Despite growing evidence of oral--systemic connections, current MI models largely exclude oral health indicators, reflecting the longstanding separation between dental and medical paradigms. This study introduces a multidomain, interpretable machine learning framework that integrates detailed periodontal and oral hygiene variables, marking one of the first efforts to quantitatively incorporate these features into MI incidence identification. A population-based case-control dataset comprising 1,355 individuals and heterogeneous variables was used to train and evaluate seven supervised classifiers via nested cross-validation. Among them, XGBoost achieved the best performance (AUC = 0.88 ± 0.01; F1 score = 0.74 ± 0.03) and was further probability-calibrated using isotonic regression, yielding a mean Brier score of 0.14 ± 0.01 and demonstrating well-aligned predicted probabilities. SHAP values confirmed the importance of conventional cardiovascular predictors, while several periodontal indicators such as mean clinical attachment loss, plaque index, and gingival bleeding emerged among the most influential features. Sex-stratified SHAP analysis revealed sex-specific patterns in the relative impact of oral features. Additionally, individual-level waterfall plots illustrated how oral inflammation may contribute independently or in combination with conventional factors to MI incidence identification. These findings support a systems-level view of periodontitis as a modifiable, biologically relevant factor in cardiovascular health and underscore the value of considering oral-health markers within screening and management frameworks.
Hussein et al. (Fri,) studied this question.