Table 5 Binary classification without and with feature selection
From: Chronic kidney disease prediction using machine learning techniques
Models | No of features | Acc | Prec | Rec | F1 | Sen | Spec |
|---|---|---|---|---|---|---|---|
RF | 18 | 99.7 | 99.9 | 99.5 | 99.7 | 99.5 | 99.9 |
RF with RFECV | 8 | 99.8 | 99.9 | 99.7 | 99.8 | 99.7 | 99.9 |
RF with UFS | 14 | 99.7 | 99.9 | 99.5 | 99.7 | 99.5 | 99.9 |
SVM | 18 | 96.9 | 99.1 | 94.7 | 96.8 | 94.7 | 99.1 |
SVM with RFECV | 9 | 95.5 | 98.7 | 92.2 | 95.3 | 92.2 | 98.8 |
SVM with UFS | 14 | 96.7 | 99.4 | 97.7 | 98.6 | 97.7 | 99.4 |
DT | 18 | 98.5 | 99.4 | 97.6 | 98.5 | 97.6 | 99.4 |
DT with RFECV | 16 | 98.6 | 99.4 | 97.7 | 98.6 | 97.7 | 99.4 |
DT with UFS | 14 | 98.5 | 99.4 | 97.6 | 98.5 | 97.6 | 99.4 |