Table 4 The performance of Kernel SVM, decision tree, random forest, KNN, and Naïve Bayes using all six ocular instrumentation features.
From: Machine learning prediction of pathologic myopia using tomographic elevation of the posterior sclera
Model | Predicted | Actual classes | Accuracy (%) | Sensitivity (%) | Specificity (%) | AUROC (%) | |
|---|---|---|---|---|---|---|---|
Healthy | PM | ||||||
Kernel SVM | Healthy | 204 | 8 | 91.47 | 80.00 | 93.58 | 86.79 |
PM | 14 | 32 | |||||
Decision Tree | Healthy | 186 | 8 | 84.50 | 80.00 | 85.32 | 82.66 |
PM | 32 | 32 | |||||
Random Forest | Healthy | 204 | 10 | 90.70 | 75.00 | 93.58 | 84.29 |
PM | 14 | 30 | |||||
KNN | Healthy | 190 | 10 | 85.27 | 75.00 | 87.16 | 81.08 |
PM | 28 | 30 | |||||
Naïve Bayes | Healthy | 196 | 9 | 87.98 | 77.50 | 89.91 | 83.70 |
PM | 22 | 31 | |||||
