Table 2.
Postoperative ICL Vault Prediction Performance of Machine Learning and Conventional Models Via Fivefold Cross Validation
| Mean Vault ± SD (µm) | MAE ± SD (µm) | MedAE (µm) | RMSE (µm) | P Value for MAE | ||
|---|---|---|---|---|---|---|
| Achieved ICL vault (target value) | 514.39 ± 174.85 | – | – | – | – | |
| Predicted ICL vault | Stacking ensemble (XGBoost + LightGBM) | 517.47 ± 125.45 | 99.67 ± 76.69 | 84.72 | 125.73 | Reference |
| Average ensemble (XGBoost + LightGBM) | 513.96 ± 126.21 | 100.46 ± 75.53 | 86.12 | 125.65 | 0.227 | |
| XGBoost (single model) | 509.78 ± 116.46 | 104.54 ± 78.70 | 89.85 | 130.82 | <0.001 | |
| Random forest | 511.36 ± 129.53 | 104.50 ± 78.47 | 87.61 | 130.65 | <0.001 | |
| Support vector machine | 511.17 ± 99.20 | 109.68 ± 87.06 | 92.92 | 140.00 | <0.001 | |
| Linear regression | 513.64 ± 120.76 | 106.63 ± 78.81 | 91.75 | 132.56 | <0.001 | |
| Manufacturer's nomogram (WTW + ACD) | 509. 34 ± 77.95 | 125.49 ± 92.10 | 110.12 | 155.62 | <0.001 | |
| NK formula22 (ACW + CLR) | 516.42 ± 77.22 | 123.58 ± 93.07 | 105.76 | 154.67 | <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.