Table 2 Classification accuracy metrics for each of the four experiments. From left to right: Low ODx Low mBR vs. High ODx Low mBR, Low ODx vs. High ODx, Low ODx vs. Intermediate and High ODx, and Low and Intermediate ODx vs. High ODx. Data for each experiment includes the AUC, best patch Voting Accuracy results, and the optimal feature ranking and classifier used to achieve the optimized patch voting accuracy results. All experiments conducted with 3-fold cross-validation
Experiment | LL vs. HH | L vs. H | L vs. Int. and H | L and Int. vs. H |
|---|---|---|---|---|
Number of Patients | 37 | 75 | 125 | 111 |
AUC | 0.81 | 0.69 | 0.58 | 0.6 |
AUC STDev | 0.08 | 0.05 | 0.03 | 0.06 |
Patch Voting Accuracy | 82% | 80% | 60% | 86% |
Best Feat. Ranking for Patch voting | MRMR-MID | PCA-VIP | Ranksum | MRMR-MID |
Best Classifier for Patch voting | LDA | Random Forest | Random Forest | Random Forest |