Table 4 Performance comparison among machine learning models
Data | ICG-R15 | SVM | RF | ExtraTrees | XGBoost | LightGBM | |
|---|---|---|---|---|---|---|---|
MRI | ICG-R15 ≤ 10% vs. ICG-R15>10% | Test set ACC | 0.824 | 0.765 | 0.824 | 0.853 | 0.794 |
Test set AUC (95%CI) | 0.802(0.639–0.965) | 0.839(0.703–0.974) | 0.873(0.743–1.000) | 0.899(0.784–1.000) | 0.806(0.650–0.962) | ||
ICG-R15 ≤ 20% vs. ICG-R15>20% | Test set ACC | 0.824 | 0.882 | 0.735 | 0.824 | 0.824 | |
Test set AUC (95%CI) | 0.893(0.780–1.000) | 0.979(0.941–1.000) | 0.878(0.739–1.000) | 0.946(0.866–1.000) | 0.833(0.632–1.000) | ||
ICG-R15 ≤ 30% vs. ICG-R15>30% | Test set ACC | 0.882 | 0.618 | 0.882 | 0.941 | 0.794 | |
Test set AUC (95%CI) | 0.922(0.802–1.000) | 0.789(0.481–1.000) | 0.945(0.866–1.000) | 0.961(0.890–1.000) | 0.891(0.743–1.000) | ||
CT | ICG-R15 ≤ 10% vs. ICG-R15>10% | Test set ACC | 0.772 | 0.632 | 0.667 | 0.842 | 0.702 |
Test set AUC (95%CI) | 0.734(0.590–0.879) | 0.661(0.514–0.807) | 0.723(0.576–0.870) | 0.822(0.700–0.944) | 0.741(0.610–0.872) | ||
ICG-R15 ≤ 20% vs. ICG-R15>20% | Test set ACC | 0.842 | 0.667 | 0.702 | 0.684 | 0.684 | |
Test set AUC (95%CI) | 0.860(0.758–0.963) | 0.722(0.591–0.853) | 0.634(0.478–0.789) | 0.709(0.570–0.847) | 0.692(0.552–0.832) | ||
ICG-R15 ≤ 30% vs. ICG-R15>30% | Test set ACC | 0.982 | 0.912 | 0.807 | 0.965 | 0.982 | |
Test set AUC (95%CI) | 0.865(0.600–1.000) | 0.871(0.683–1.000) | 0.783(0.471–1.000) | 0.938(0.824–1.000) | 0.925(0.776–1.000) |