Table 3. Shows the efficacy of the machine learning algorithms in predicting cryotherapy outcome.
| AI method | Accuracy | Precision | Recall | FPR | FNR | TNR | TPR | F |
|---|---|---|---|---|---|---|---|---|
| SVM | 92.2 | 87.5 | 97.7 | 12.8 | 2.3 | 87.4 | 88.3 | 92.3 |
| CVM | 97.8 | 97.9 | 97.9 | 2.4 | 2.1 | 97.6 | 99.1 | 97.9 |
| RF | 100 | 100 | 100 | 0 | 0 | 100 | 100 | 100 |
| k-NN | 93.3 | 93.8 | 93.8 | 7.1 | 6.3 | 92.9 | 93.9 | 93.8 |
| MLP | 93.3 | 91.7 | 95.7 | 9.1 | 4.3 | 90.9 | 91.9 | 93.6 |
| BLR | 91.1 | 87.5 | 95.5 | 13.0 | 4.5 | 87.0 | 88.1 | 91.3 |
Values are presented as percentage. AI: artificial intelligence, FPR: false-positive rate, FNR: false-negative rate, TNR: true-negative rate, TPR: true-positive rate, SVM: support vector machines, CVM: core vector machines, RF: random forest, k-NN: k-nearest neighbours, MLP: multilayer perceptron, BLR: binary logistic regression