Table 5.
Illustrates performance metrics, including accuracy (ACC), sensitivity (SEN), specificity (SPE), precision (PR), false positive rate (FPR), F1 scores and ROC curve (AUC), for all models in the context of the 4-chamber view.
| Model | ACC | SEN | SPE | PR | FPR | F1 score | AUC |
|---|---|---|---|---|---|---|---|
| SVM | 0.88 | 0.67 | 0.85 | 1.00 | 0 | 0.80 | 0.68 |
| KNN | 0.83 | 0.60 | 0.81 | 0.89 | 0.03 | 0.72 | 0.58 |
| RUS boosted | 0.71 | 0.53 | 0.76 | 0.60 | 0.19 | 0.56 | 0.50 |
| RF | 0.75 | 0.64 | 0.80 | 0.64 | 0.19 | 0.64 | 0.46 |
*Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Under-Sampling Boosted (RUS boosted), Random Forest (RF).