Table 11.
Performance comparison of recent prediction models on UCI CKD dataset. Classifiers with ’*’ represent the classifiers with the highest ACC scores in the respective paper
| Work Ref. | Classifier | Performance metrics | |||||
|---|---|---|---|---|---|---|---|
| ACC | P | R | F1 | S | AUC | ||
| Pujianto et al. [167] | SVM | 1.00 | – | – | – | – | – |
| Amirgaliyev et al. [15] | SVM | 0.94 | – | 0.94 | – | 0.95 | – |
| Chetty et al. [41] | k-NN* | 1.00 | – | – | – | – | – |
| Charleonnan et al. [35] | SVM | 0.98 | – | 0.99 | – | 0.98 | – |
| Wibawa et al. [226] | AB | 0.98 | 0.98 | 0.98 | 0.98 | – | – |
| Sedighi et al. [190] | NB | 0.97 | – | – | – | – | – |
| Sisodia and Verma [207] | RF | 1.00 | 1.00 | 1.00 | 1.00 | – | 1.00 |
| Avci et al. [21] | DT | 0.99 | 0.98 | 0.99 | 0.98 | – | – |
| Almansour et al. [12] | ANN | 0.99 | – | – | – | – | – |
| Wibawa et al. [225] | SVM | – | – | 0.99 | – | 1.00 | – |
| Aljaaf et al. [11] | MLP | 0.98 | – | – | 0.98 | – | 0.99 |
| Gunarathne et al. [76] | RF | 0.99 | – | – | – | – | – |
| Guia et al. [74] | k-NN | – | – | – | 0.99 | – | – |
| Johari et al. [108] | ANN | 1.00 | 1.00 | 1.00 | – | – | – |
| Polat et al. [164] | SVM | 0.98 | 0.98 | 0.98 | – | – | 0.98 |
| Harimoorthy and Thangavelu [81] | Improved SVM | 0.98 | 0.95 | 1.00 | – | 0.99 | – |