Table 2. Shows the efficacy of the machine learning algorithms in predicting Immunotherapy outcome.
| AI method | Accuracy | Precision | Recall | FPR | FNR | TNR | TPR | F |
|---|---|---|---|---|---|---|---|---|
| SVM | 79.0 | 100 | 78.9 | 0 | 21.1 | 96.1 | 98.3 | 88.2 |
| CVM | 95.6 | 100 | 94.7 | 0 | 5.3 | 100 | 97.1 | 97.3 |
| RF | 100 | 100 | 100 | 0 | 0 | 100 | 100 | 100 |
| k-NN | 83.0 | 100 | 82.6 | 0 | 17.5 | 100 | 98.2 | 90.4 |
| MLP | 89.0 | 98.6 | 88.6 | 9.1 | 11.4 | 90.9 | 95.9 | 93.3 |
| BLR | 85.0 | 100 | 84.5 | 0 | 15.5 | 100 | 98.1 | 91.6 |
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.