Table 3 The performance of RF and NB for additional testing
From: Prediction of postoperative complications of pediatric cataract patients using data mining
Problem | Algorithm | Accuracy | Sensitivity | Specificity |
|---|---|---|---|---|
Whether a patient suffers from complications | Random forest | 0.700 | 0.625 | 0.769 |
Naïve Bayesian | 0.700 | 0.731 | 0.667 | |
Whether a patient suffers from SLPVA | Random forest | 0.720 | 0.667 | 0.722 |
Naïve Bayesian | 0.660 | 0.611 | 0.688 | |
Whether a patient suffers from AHIP | Random forest | 0.700 | 0.636 | 0.718 |
Naïve Bayesian | 0.660 | 0.545 | 0.692 |