Figure 5
Main clinical predictors for xerostomia when using machine learning algorithms. (A, B) show analysis of all participants’ data, while (C, D) show the analysis of Group 3 (60–100 years). (A, C) The control condition (gray bar) input only two kinds of information: UFR and SFR, while the experimental condition (blue bar) input information on sex, age, number of systemic diseases, and number of medications, in addition to UFR and SFR. AUC values are shown for four machine learning algorithms. Logistic regression analysis (B, D) indicates the estimated weight (log-odds ratio) values of significant predictors. LR, Logistic Regression; LDA, Linear Discriminant Analysis; KNN, K-Nearest Neighbors; MLP, Multilayer Perceptron; AUC, area under the curve; UFR, unstimulated salivary flow rate; SFR, stimulated salivary flow rate. Statistical significance was set at p < 0.05, * p < 0.05, and ** p < 0.01.
