Table 12.
Performance metric comparison of various classifiers.
| Classification techniques | Performance metric parameters | |||||
|---|---|---|---|---|---|---|
| Precision | Accuracy | Sensitivity | Specificity | Recall | F-measure | |
| Random forest | 88.07 | 88.78 | 87.91 | 87.1 | 85.31 | 87.89 |
| Decision tree | 89.07 | 89.78 | 88.91 | 88.1 | 86.31 | 88.89 |
| Support vector machine | 86.07 | 86.78 | 85.91 | 85.1 | 83.31 | 85.89 |
| XGBoost | 87.07 | 87.78 | 86.91 | 86.1 | 84.31 | 86.89 |
| Radial basis functions | 90.07 | 90.78 | 89.91 | 89.1 | 87.31 | 89.89 |
| K-nearest neighbour | 79.07 | 79.78 | 78.91 | 78.1 | 76.31 | 78.89 |
| Proposed learning vector quantization | 98.07 | 98.78 | 97.91 | 97.1 | 95.31 | 97.89 |
| Naive Bayes | 94.07 | 94.78 | 93.91 | 93.1 | 91.31 | 93.89 |
Significant values are in bold.