Table 3 Performance metrics of the LACE model and machine learning models based on the testing set with fivefold cross-validation (Mean ± Standard Deviation, Unit: %)
Model (#Features) | Precision | Recall | F1-Score | AUROC | AUPRC |
|---|---|---|---|---|---|
LACE (4) | 2.97 ± 0.15 | 68.67 ± 3.86 | 5.70 ± 0.29 | 70.58 ± 1.88 | 34.63 ± 0.00 |
Logistic Regression: original features (70) | 45.76 ± 15.72 | 4.00 ± 2.00 | 7.35 ± 3.59 | 80.46 ± 2.43 | 10.26 ± 2.23 |
Logistic Regression: original features (27) | 43.62 ± 20.73 | 5.00 ± 1.05 | 8.84 ± 2.00 | 82.88 ± 3.57 | 11.66 ± 3.54 |
Random Forest: original features (70) | 100.00 ± 0.00 | 41.33 ± 3.86 | 58.39 ± 3.79 | 97.89 ± 0.71 | 70.15 ± 4.23 |
Xgboost: original features (70) | 93.23 ± 5.35 | 45.67 ± 3.89 | 61.25 ± 4.32 | 97.95 ± 0.52 | 66.52 ± 2.23 |
Catboost 1 (C1): original features (70) | 93.77 ± 4.05 | 53.33 ± 5.27 | 67.80 ± 4.47 | 99.03 ± 0.07 | 75.15 ± 1.92 |
Catboost 2: features in C1 with importance > 0.5 (35) | 95.12 ± 2.54 | 56.00 ± 5.33 | 70.29 ± 3.84 | 99.04 ± 0.09 | 76.11 ± 2.45 |
Catboost 3: features in C1 with importance > 0.6 (28) | 95.09 ± 3.09 | 55.33 ± 5.31 | 69.74 ± 3.99 | 99.08 ± 0.08 | 76.69 ± 1.85 |
Catboost 4: features in C1 with importance > 0.8 (21) | 94.70 ± 3.52 | 56.00 ± 6.02 | 70.10 ± 4.40 | 99.09 ± 0.08 | 77.11 ± 1.93 |
Catboost 5: features in C1 with importance > 0.9 (19) | 93.20 ± 1.59 | 55.33 ± 5.72 | 69.29 ± 4.76 | 99.07 ± 0.10 | 76.80 ± 1.64 |
Catboost 6: features in C1 with importance > 1.1 (14) | 91.46 ± 2.12 | 56.67 ± 4.47 | 69.86 ± 3.51 | 99.00 ± 0.11 | 76.97 ± 2.90 |