Fig. 4

Performance evaluation of seven predictive models based on salivary microbiota features. A–G. Normalized confusion matrices and corresponding AUC for LR (A), SVM (B), MLP (C), NB (D), RF (E), GBDT (F), and LightGBM (G). The confusion matrices consist of False Positives, False Negatives, True Positives, and True Negatives. H Comparison of AUC among the seven predictive models, with AUC reflecting the performance of the binary classification models. I Comparison of the F1 scores among the seven predictive models. J Comparison of the Precision-Recall Curves among the seven predictive models. F1 score and Precision-Recall Curve are comprehensive metrics for evaluating model performance, with a larger area under the curve indicating better and more stable model performance. LR logistic regression, SVM support vector machine, MLP multi-layer perceptron, NB naïve Bayes, RF random forest, GBDT gradient boosting decision tree, LightGBM Light Gradient Boosting Machine, AUC the area under the receiver operating characteristic curve