Table 3.
Postoperative ICL Vault Prediction Performance of Machine Learning and Conventional Models in the Internal Validation Dataset From the Korean Patients (Internal Validation)
| Mean Vault ± SD (µm) | MAE ± SD (µm) | MedAE (µm) | RMSE (µm) | P Value for MAE | ||
|---|---|---|---|---|---|---|
| Achieved ICL vault (target value) | 516.82 ± 195.98 | – | – | – | – | |
| Predicted ICL vault | Stacking ensemble (XGBoost + LightGBM) | 517.76 ± 134.52 | 106.88 ± 90.67 | 82.91 | 140.14 | Reference |
| Average ensemble (XGBoost + LightGBM) | 517.18 ± 127.31 | 107.40 ± 98.49 | 83.09 | 145.69 | 0.678 | |
| XGBoost (single model) | 514.72 ± 124.08 | 110.33 ± 100.31 | 84.50 | 149.08 | 0.018 | |
| Random forest | 517.45 ± 123.89 | 110.74 ± 100.35 | 85.05 | 149.42 | 0.008 | |
| Support vector machine | 518.92 ± 103.71 | 123.78 ± 114.30 | 97.54 | 168.44 | <0.001 | |
| Linear regression | 517.10 ± 115.21 | 112.31 ± 99.67 | 86.78 | 150.14 | <0.001 | |
| Manufacturer's nomogram (WTW + ACD) | 520.11 ± 79.91 | 138.16 ± 114.89 | 118.54 | 179.65 | <0.001 | |
| NK formula22 (ACW + CLR) | 515.48 ± 79.98 | 133.12 ± 121.12 | 107.56 | 179.93 | <0.001 | |
ACD, anterior chamber depth; ACW, anterior chamber width; CLR, crystalline lens rise; ICL, implantable collamer lens; LightGBM, light gradient boosting machine; MAE, mean absolute prediction error; MedAE, median absolute prediction error; RMSE, root mean square error; SD, standard deviation; WTW, white-to-white.