Table 3. The performance of different machine learning models in PMI3.
| Models | Drop | Mean | |||||||
|---|---|---|---|---|---|---|---|---|---|
| SVM | LR | RF | ANN | SVM | LR | RF | ANN | ||
| Accuracy | 0.69 | 0.71 | 0.71 | 0.72 | 0.69 | 0.70 | 0.72 | 0.70 | |
| Sensitivity | 0.73 | 0.67 | 0.70 | 0.72 | 0.73 | 0.66 | 0.70 | 0.70 | |
| PPV | 0.67 | 0.72 | 0.71 | 0.71 | 0.67 | 0.71 | 0.72 | 0.72 | |
| F1-score | 0.69 | 0.69 | 0.70 | 0.72 | 0.70 | 0.68 | 0.71 | 0.71 | |
| AUC | 0.76 | 0.76 | 0.77 | 0.77 | 0.76 | 0.76 | 0.77 | 0.76 | |
SVM, Support Vector Machine; LR, Logistic Regression; RF, Random Forest; ANN, artificial neural network; PPV, positive predictive value; AUC, area under curve.