Table 4 Comparison with the state of the art for PIMA and BRFSS datasets
From: Diabetes type 2 classification using machine learning algorithms with up-sampling technique
No. | Authors | Models | PIMA—Accuracy | Authors | Models | BRFSS—Accuracy |
|---|---|---|---|---|---|---|
1 | Lu et al. [5] | Random Forest | 84.95 | Dinh et al. [16] | eXtreme Gradient Boost | 95 |
2 | Mujumdar et al. [6] | Linear Discriminate Analysis | 77 | Maniruzzaman et al. [15] | Linear Regression + RandomForest | 94.25% |
3 | Massari et al. [9] | Ontology classifiers and SVM | 77.5 | Nadeem et al. [14] | SVM + ANN | 94.67% |
4 | Farajollahi et al. [10] | AdaBoost Classifier | 83 | |||
5 | Sivaranjani et al. [12] | Random Forest | 83 | |||
| Â | Proposed Model | Extra Tree Classifier | 89 | Proposed Model | Extra Tree Classifier | 96 |