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.