Table 1 |
|||||||||
| Overall performance measure of classification algorithms on datasets | |||||||||
| Algorithms | T1D | Az | Ab | Asthma | A & B | A & C | B & D | Avg. | Rank |
| Naïve Bayes | 92.0 | 93.4 | 91.5 | 77.7 | 90.8 | 93.5 | 93.6 | 90.4 | 1 |
| MLP | 90.1 | 92.7 | 90.2 | 71.1 | 84.7 | 92.7 | 89.3 | 87.3 | 2 |
| SVM | 91.6 | 88.0 | 90.7 | 71.3 | 86.1 | 88.4 | 93.1 | 87.0 | 3 |
| VFI | 90.5 | 92.2 | 75.5 | 62.6 | 87.7 | 93.4 | 92.7 | 84.9 | 4 |
| Hyper Pipes | 89.8 | 89.7 | 81.3 | 62.3 | 82.0 | 86.6 | 87.8 | 82.8 | 5 |
| R. Forest | 91.5 | 82.4 | 93.3 | 62.8 | 80.6 | 81.4 | 81.1 | 81.9 | 6 |
| Bayes Net | 90.3 | 87.7 | 92.5 | 53.9 | 80.2 | 83.2 | 85.1 | 81.8 | 7 |
| K-means | 88.3 | 91.8 | 80.7 | 59.6 | 77.8 | 83.3 | 83.6 | 80.7 | 8 |
| Logistic R. | 90.6 | 93.3 | 60.4 | 50.7 | 81.5 | 84.8 | 90.7 | 78.9 | 9 |
| SLR | 92.2 | 71.8 | 90.1 | 72.2 | 65.0 | 68.5 | 84.7 | 77.8 | 10 |
| KNN | 91.4 | 81.5 | 52.5 | 55.8 | 87.5 | 75.7 | 89.0 | 76.2 | 11 |
| K star | 81.9 | 90.7 | 89.4 | 53.5 | 64.3 | 68.8 | 70.7 | 74.2 | 12 |
| M5P | 85.1 | 58.7 | 83.2 | 60.0 | 75.2 | 73.4 | 79.6 | 73.6 | 13 |
| J48 | 80.3 | 69.7 | 78.4 | 48.7 | 70.6 | 68.4 | 76.7 | 70.4 | 14 |
| Random Tree | 83.8 | 71.7 | 76.2 | 52.9 | 69.3 | 60.8 | 75.0 | 70.0 | 15 |
| ASC | 76.8 | 70.0 | 77.9 | 43.1 | 72.0 | 63.1 | 76.7 | 68.5 | 16 |
| LDA | 69.7 | 52.0 | 89.1 | 70.8 | 62.8 | 69.7 | 52.6 | 66.7 | 17 |
T1D: Type 1 diabetes datasets, Az: Alzehemer’s dataset, Ab: Antibodies dataset. Table showing algorithms overall performance in each datasets based on average score. Score >90% are marked in bold. Naïve Bayes scored the overall highest average score of 90.4%.
Kukreja et al. BMC Bioinformatics 2012 13:139 doi:10.1186/1471-2105-13-139
