Table 5 MIAS Dataset Cross-Validation Performance Metrics.
From: A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM
Model | ACC | SEN | SPC | PRE | F1-S | MCC |
|---|---|---|---|---|---|---|
5-CV | ||||||
RF | 0.85 | 0.84 | 0.86 | 0.85 | 0.85 | 0.83 |
KNN | 0.87 | 0.88 | 0.89 | 0.86 | 0.87 | 0.85 |
DT | 0.83 | 0.81 | 0.85 | 0.84 | 0.82 | 0.80 |
NB | 0.86 | 0.87 | 0.88 | 0.85 | 0.86 | 0.84 |
LR | 0.88 | 0.87 | 0.90 | 0.89 | 0.88 | 0.86 |
AB | 0.84 | 0.83 | 0.87 | 0.86 | 0.85 | 0.82 |
GB | 0.89 | 0.90 | 0.91 | 0.88 | 0.89 | 0.87 |
SVM | 0.89 | 0.88 | 0.90 | 0.87 | 0.88 | 0.85 |
BGWO-SQSVM | 0.92 | 0.91 | 0.94 | 0.93 | 0.92 | 0.90 |
Q-BGWO-SQSVM | 0.97 | 0.96 | 0.98 | 0.97 | 0.97 | 0.95 |
10-CV | ||||||
RF | 0.87 | 0.86 | 0.89 | 0.88 | 0.87 | 0.85 |
KNN | 0.88 | 0.89 | 0.90 | 0.87 | 0.88 | 0.86 |
DT | 0.84 | 0.82 | 0.86 | 0.85 | 0.83 | 0.81 |
NB | 0.87 | 0.88 | 0.89 | 0.86 | 0.87 | 0.85 |
LR | 0.90 | 0.89 | 0.92 | 0.91 | 0.90 | 0.88 |
AB | 0.85 | 0.84 | 0.88 | 0.87 | 0.86 | 0.83 |
GB | 0.91 | 0.92 | 0.93 | 0.90 | 0.91 | 0.89 |
SVM | 0.92 | 0.91 | 0.93 | 0.92 | 0.92 | 0.90 |
BGWO-SQSVM | 0.94 | 0.93 | 0.96 | 0.95 | 0.94 | 0.92 |
Q-BGWO-SQSVM | 0.98 | 0.97 | 0.99 | 0.98 | 0.98 | 0.96 |
15-CV | ||||||
RF | 0.88 | 0.87 | 0.90 | 0.89 | 0.88 | 0.86 |
KNN | 0.89 | 0.90 | 0.91 | 0.88 | 0.89 | 0.87 |
DT | 0.85 | 0.83 | 0.87 | 0.86 | 0.84 | 0.82 |
NB | 0.88 | 0.89 | 0.90 | 0.87 | 0.88 | 0.86 |
LR | 0.91 | 0.90 | 0.93 | 0.92 | 0.91 | 0.89 |
AB | 0.86 | 0.85 | 0.89 | 0.88 | 0.87 | 0.84 |
GB | 0.92 | 0.93 | 0.94 | 0.91 | 0.92 | 0.90 |
SVM | 0.93 | 0.92 | 0.94 | 0.93 | 0.93 | 0.91 |
BGWO-SQSVM | 0.95 | 0.94 | 0.97 | 0.96 | 0.95 | 0.93 |
Q-BGWO-SQSVM | 0.98 | 0.96 | 0.99 | 0.97 | 0.98 | 0.97 |