Table 3.
Evaluation Metrics for the Metabolomics‐Only and the Combined Metabolomics–Clinical Models With and Without OGTT Variables Applied to the Training and Test Sets
| Classifier | Metabolomics‐Only Model | Combined Model #1 (With OGTT Variables) | Combined Model #2 (Without OGTT Variables) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| AUROC | Sensitivity | Specificity | AUROC | Sensitivity | Specificity | AUROC | Sensitivity | Specificity | |
| Training Set | |||||||||
| Naive Bayes | 0.847 | 77% | 78% | 0.880 | 82% | 77% | 0.866 | 81% | 81% |
| Random forest | 0.846 | 60% | 84% | 0.924 | 77% | 90% | 0.923 | 72% | 91% |
| Logistic | 0.860 | 68% | 83% | 0.875 | 75% | 87% | 0.876 | 73% | 88% |
| Test Set | |||||||||
| Naive Bayes | 0.825 | 73% | 78% | 0.848 | 73% | 75% | 0.842 | 73% | 76% |
| Random forest | 0.818 | 60% | 87% | 0.944 | 73% | 97% | 0.922 | 73% | 94% |
| Logistic | 0.839 | 69% | 81% | 0.863 | 72% | 83% | 0.858 | 70% | 81% |
Bold values indicate the classifier with the highest AUROC for each model in the training or test set.
Abbreviations: AUROC, Area under the receiver operator curve; OGTT, Oral glucose tolerance test.