Table 4:
A summary of prediction performance of 4 ML methods (average of 100 iterations) using stratified k-fold cross-validation. The (relative) performance enhancements are provided by the underlined numbers in the squared brackets. Each ML model with Velocity-informatics includes base variables and additional variables.
| SVM (Baseline Variables: Aneurysm Location, Parent Vessel Diameter, Ostium min, NRV2) | |||
|---|---|---|---|
|
| |||
| Without Velocity-informatics | With Velocity-informatics (Additional Variables: IMC2, GLCM.DE, GLRLM.LongRunEmphaasis) | ||
| AUC | 0.78(0.77–0.78) | AUC | 0.81(0.80–0.81) [0.03] |
| Mean Accuracy | 70.20% | Mean Accuracy | 74.30% [4.1%] |
| Mean Ruptured Accuracy | 51.00% | Mean Ruptured Accuracy | 54.75% [4.75%] |
| Mean Unruptured Accuracy | 88.30% | Mean Unruptured Accuracy | 87.33% [−0.97%] |
| GLM (Baseline Variables: Aneurysm Location, Parent Vessel Diameter, Ostium min, NRV2) | |||
|---|---|---|---|
|
| |||
| Without Velocity-informatics | With Velocity-informatics (Additional Variables: GLCM.DE, GLRLM.LongRunEmphaasis, GLCM.DA, GLSZM.ZP) | ||
| AUC | 0.79(0.79–0.80) | AUC | 0.82(0.82–0.83) [0.03] |
| Mean Accuracy | 72.80% | Mean Accuracy | 75.80% [3.00%] |
| Mean Ruptured Accuracy | 50.25% | Mean Ruptured Accuracy | 66.50% [16.25%] |
| Mean Unruptured Accuracy | 87.85% | Mean Unruptured Accuracy | 82.00% [−5.85%] |
| GLMNet (Baseline Variables: Aneurysm Location, Parent Vessel Diameter, Ostium min, NRV2) | |||
|---|---|---|---|
|
| |||
| Without Velocity-informatics | With Velocity-informatics (Additional Variables: IMC2, GLCM.DE, GLRLM.LongRunEmphaasis) | ||
| AUC | 0.79(0.79–0.80) | AUC | 0.81(0.80–0.81) [0.02] |
| Mean Accuracy | 72.00% | Mean Accuracy | 74.00% [2.00%] |
| Mean Ruptured Accuracy | 51.00% | Mean Ruptured Accuracy | 53.00% [2.00%] |
| Mean Unruptured Accuracy | 86.00% | Mean Unruptured Accuracy | 88.50% [2.50%] |
| RF (Baseline Variables: Aneurysm Location, Parent Vessel Diameter, Ostium min, NRV2) | |||
|---|---|---|---|
|
| |||
| Without Velocity-informatics | With Velocity-informatics (Additional Variables: Id, GLCM.DE, GLRLM.LongRunEmphaasis, GLSZM.ZP) | ||
| AUC | 0.75(0.74–0.75) | AUC | 0.79(0.78–0.79) [0.04] |
| Mean Accuracy | 70.70% | Mean Accuracy | 77.50% [6.80%] |
| Mean Ruptured Accuracy | 60.25% | Mean Ruptured Accuracy | 69.50% [9.25%] |
| Mean Unruptured Accuracy | 71.50% | Mean Unruptured Accuracy | 82.33% [10.83%] |
As described in Table 1, DA – difference average, DE – difference entropy, DV – difference variance, Id – inverse difference, Idm – inverse difference moment, ZP – zone percentage.