Table 4. The performance of different machine learning models in PMI5.
| Models | Drop | Mean | |||||||
|---|---|---|---|---|---|---|---|---|---|
| SVM | LR | RF | ANN | SVM | LR | RF | ANN | ||
| Accuracy | 0.60 | 0.62 | 0.67 | 0.62 | 0.61 | 0.62 | 0.61 | 0.61 | |
| Sensitivity | 0.62 | 0.62 | 0.68 | 0.56 | 0.66 | 0.62 | 0.66 | 0.61 | |
| PPV | 0.58 | 0.59 | 0.59 | 0.62 | 0.58 | 0.59 | 0.58 | 0.61 | |
| F1-score | 0.60 | 0.61 | 0.63 | 0.59 | 0.62 | 0.61 | 0.62 | 0.61 | |
| AUC | 0.66 | 0.65 | 0.67 | 0.65 | 0.66 | 0.65 | 0.67 | 0.65 | |
SVM, Support Vector Machine; LR, Logistic Regression; RF, Random Forest; ANN, Artificial neural network; PPV, positive predictive value; AUC, area under curve.