Table 3.
Pupil Expansion Device Usage Prediction Performance of Machine Learning Models
| Models | Preoperative Pupil Size |
Pupil Size Timecourse |
||
|---|---|---|---|---|
| Accuracy (%) | AUC (%) | Accuracy (%) | AUC (%) | |
| Naïve Bayes | 83.33 | 87.00 | 93.33 | 93.33 |
| KNN | 76.67 | 87.33 | 83.33 | 89.44 |
| Logistic Regression | 83.33 | 87.22 | 90.00 | 97.74 |
| Decision Tree | 73.33 | 73.33 | 93.33 | 93.33 |
| SVM | 83.33 | 87.00 | 96.67 | 99.44 |
| Random Forest | 73.33 | 86.56 | 96.67 | 99.33 |
Bold fonts indicate the better performance.
AUC = area under the curve; KNN = K-Nearest Neighbors; SVM = Support Vector Machine.