Table 3. Comparing Classification Accuracy of SAIRS2 With Other Classifiers of Tuberculosis a.
| Method | Classification Accuracy, % | Sensitivity, % | Specificity, % | J Index | AUC | RMSE |
|---|---|---|---|---|---|---|
| SAIRS2 | 100.00 | 100.00 | 100.00 | 1 | 1 | 0 |
| AIRS2 | 100.00 | 100.00 | 100.00 | 1 | 1 | 0 |
| MLP | 100.00 | 100.00 | 100.00 | 1 | 1 | 0.003 |
| Naive Bayes | 100.00 | 100.00 | 100.00 | 1 | 1 | 0 |
| J48 | 100.00 | 100.00 | 100.00 | 1 | 1 | 0 |
| KNN, K=7 | 100.00 | 100.00 | 100.00 | 1 | 1 | 0 |
| RBF Classifier | 100.00 | 100.00 | 100.00 | 1 | 1 | 0.054 |
| Hierarchal LVQ | 94.83 | 92.11 | 100.00 | 0.96 | 0.96 | 0.227 |
| Spegasos | 99.82 | 100.00 | 99.50 | 1 | 0.99 | 0.006 |
| Random Forest | 99.43 | 100.00 | 100.00 | 1 | 0.98 | 0.025 |
| Ridor | 99.43 | 99.11 | 100.00 | 1 | 0.99 | 0.024 |
| Lib Linear | 98.33 | 97.44 | 100.00 | 1 | 0.96 | 0.067 |
| Random Tree | 98.21 | 98.7 | 97.30 | 0.98 | 0.98 | 0.066 |
| LVQ with kNN | 97.13 | 92.11 | 100.00 | 0.96 | 0.96 | 0.227 |
| Multi pass LVQ | 95.98 | 93.86 | 100.00 | 0.97 | 0.96 | 0.2 |
| LVQ | 94.83 | 92.11 | 100.00 | 0.96 | 0.96 | 0.227 |
| CLOANALG-CSCA | 93.68 | 90.35 | 100.00 | 0.95 | 0.95 | 0.251 |
| CLONALG | 92.52 | 92.27 | 93.00 | 0.93 | 0.92 | 0.217 |
| SMO | 89.66 | 91.59 | 98.31 | 0.89 | 1 | 0.221 |
| Zero R | 65.49 | 100.00 | 0.00 | 0.5 | 0.5 | 0.475 |
| Lib SVM | 65.49 | 100.00 | 0.00 | 0.5 | 0.5 | 0.227 |
a Abbreviations: AUC, Area Under ROC Curve; CLONALG, The CLONal selection ALGorithm; CSCA, The Clonal Selection Classification Algorithm; kNN, k-Nearest Neighbor; LVQ, Learning Vector Quantization; MLP, Multi-Layer Perceptron; SMO, Sequential Minimal Optimization; RMSE, root mean-squared errors.