Table 4.
Model | AUROC | 1-Accuracy | Sensitivity | Specificity | MCC |
---|---|---|---|---|---|
Variable selection by logistic regression using backward elimination * | |||||
Logistic Regression (Final model) | 0.784 | 0.235 | 0.799 | 0.531 | 0.258 |
XGBoost | 0.785 | 0.237 | 0.769 | 0.581 | 0.148 |
Random Forest | 0.780 | 0.245 | 0.759 | 0.400 | 0.040 |
LightGBM | 0.774 | 0.236 | 0.782 | 0.546 | 0.204 |
Variable Selection by random forest using permutation importance ** | |||||
Logistic Regression | 0.783 | 0.232 | 0.798 | 0.547 | 0.260 |
XGBoost | 0.782 | 0.237 | 0.770 | 0.583 | 0.158 |
Random Forest | 0.779 | 0.245 | 0.772 | 0.480 | 0.139 |
LightGBM | 0.780 | 0.239 | 0.776 | 0.532 | 0.174 |
Analyses were performed with test dataset (n = 1259). AUROC: area under the receiver operating characteristic; 1-Accuracy: misclassification rate; MCC: Matthews correlations coefficient. * Five variables were included: age, household income, daily brushing frequency, age of the mother at giving birth, and the mother’s DMFT quartile. ** Four variables were included: age, household income, daily brushing frequency, and the mother’s DMFT quartile.