Table 3.
Comparison of accuracy rate of various classification models with and without RFE in case 2.
| Missing field | Classifier | Without feature selection | Feature selection method |
|||
|---|---|---|---|---|---|---|
| DT | SVM | RF | LR | |||
| Age | ANN | 0.7303 | 0.9942 | 0.9914 | 0.7246 | 0.9714 |
| RF | 0.9572 | 1 | 1 | 0.9957 | 1 | |
| SVM | 0.8159 | 1 | 1 | 1 | 1 | |
| Gender | ANN | 0.7303 | 0.9942 | 0.9643 | 0.8288 | 0.7303 |
| RF | 0.9572 | 1 | 0.9986 | 0.8131 | 0.7475 | |
| SVM | 0.9372 | 1 | 1 | 0.7303 | 0.7303 | |
| Jaundice | ANN | 0.9514 | 0.9942 | 0.9671 | 0.8901 | 0.98 |
| RF | 0.9572 | 1 | 0.9971 | 0.9957 | 1 | |
| SVM | 0.9514 | 1 | 1 | 1 | 1 | |
| Autism | ANN | 0.9514 | 0.9942 | 0.99 | 0.7303 | 0.9871 |
| RF | 0.9572 | 1 | 1 | 0.8146 | 1 | |
| SVM | 0.9514 | 1 | 1 | 0.7303 | 1 | |
| used_app_before | ANN | 0.7303 | 0.9928 | 0.99 | 0.9058 | 0.7303 |
| RF | 0.9572 | 1 | 1 | 0.9957 | 0.7475 | |
| SVM | 0.9514 | 1 | 1 | 1 | 0.7303 | |
| Type 1 | ANN | 0.8159 | 0.9928 | 0.9928 | 0.7303 | 0.7303 |
| RF | 0.9543 | 1 | 1 | 0.8245 | 0.7475 | |
| SVM | 0.8159 | 1 | 1 | 0.7303 | 0.7303 | |
| Type 2 | ANN | 0.8188 | 0.9942 | 0.7303 | 0.7303 | 0.9843 |
| RF | 0.9543 | 1 | 0.7532 | 0.8188 | 1 | |
| SVM | 0.8188 | 1 | 0.7303 | 0.7303 | 1 | |
| Type 3 | ANN | 0.7646 | 0.9942 | 0.9671 | 0.7631 | 0.7303 |
| RF | 0.9572 | 1 | 0.9986 | 0.9957 | 0.7475 | |
| SVM | 0.7646 | 1 | 1 | 1 | 0.7303 | |
Abbreviations: DT, decision tree; SVM, support vector machine; RF, random forest; LR, logistic regression; ANN, artificial neural network; RFE, recursive feature elimination.