Table 2.
Model performance.
| Model | PTB vs. FTB | SPTB vs. FTB | ||||||
|---|---|---|---|---|---|---|---|---|
| Accuracy | AUC | Specificity | Sensitivity | Accuracy | AUC | Specificity | Sensitivity | |
| LR | 0.68 | 0.72 | 0.64 | 0.74 | 0.81 | 0.86 | 0.92 | 0.64 |
| DT | 0.64 | 0.64 | 0.64 | 0.63 | 0.76 | 0.78 | 0.80 | 0.75 |
| NB | 0.59 | 0.78 | 0.92 | 0.23 | 0.78 | 0.87 | 0.78 | 0.76 |
| RF | 0.65 | 0.73 | 0.61 | 0.72 | 0.83 | 0.86 | 0.96 | 0.66 |
| SVM | 0.47 | 0.40 | 0.05 | 0.96 | 0.66 | 0.62 | 1.00 | 0.00 |
| ANN | 0.58 | 0.63 | 0.58 | 0.60 | 0.70 | 0.71 | 0.83 | 0.45 |
Results from random forest analysis are highlighted in Bold.
LR, logistic regression; DT, decision tree; NB, naïve Bayes; RF, random forest; SVM, support vector machine; ANN, artificial neural network; AUC, area under the curve; PTB, preterm birth; FTB, full-term birth; SPTB, spontaneous preterm birth.