Table 10.
Performance comparison of recent prediction models on PIMA Indians Diabetes 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 | ||
| Patil et al. [160] | DT | – | – | 0.79 | – | 0.93 | 0.95 |
| NirmalaDevi et al. [154] | k-NN | 0.97 | – | 0.97 | – | 0.97 | – |
| Chen et al. [39] | DT | 0.90 | – | 0.87 | – | 0.91 | – |
| Wu et al. [227] | LR | 0.95 | 0.95 | 0.95 | – | – | 0.98 |
| Sisodia and Sisodia [206] | NB | 0.76 | 0.76 | 0.76 | 0.76 | – | 0.82 |
| Dutta et al. [54] | RF | – | – | 0.84 | 0.84 | – | – |
| Zhu et al. [237] | LR* | 0.97 | 0.97 | 0.97 | – | – | – |
| Daanouni et al. [46] | DNN | 0.90 | – | – | – | – | – |
| Rakshit et al. [173] | ANN | 0.83 | – | – | – | – | – |
| Ashiquzzaman et al. [19] | MLP | 0.88 | – | – | – | – | – |
| Saji and Balachandran [182] | MLP | 0.70 | – | – | – | – | – |
| Jahangir et al. [102] | MLP | 0.89 | 0.85 | 0.88 | – | – | – |
| Hasan et al. [82] | AB+XGB | – | – | 0.79 | – | 0.93 | 0.95 |
| Kannadasan et al. [111] | Encoder part of autoencoder | 0.86 | 0.90 | 0.87 | 0.89 | 0.83 | – |
| Vaishali et al. [221] | MOE NSGA II | 0.83 | – | – | – | – | – |
| Mansourypoor and Asadi [135] | RLEFRBS* | 0.84 | – | – | – | – | – |
| Harimoorthy and Thangavelu [81] | Improved SVM | 0.99 | 1.00 | 0.95 | – | 1.00 | – |