Table 2A Model performance in validation set (n = 33).

From: Synthetic data generation method improves risk prediction model for early tumor recurrence after surgery in patients with pancreatic cancer

Model

Training Data

Accuracy

Sensitivity

Specificity

AUC-ROC

F1-score

GBM

Original

0.82 (0.70–0.92)

0.80 (0.65–0.92)

0.83 (0.66–0.95)

0.82 (0.68–0.93)

0.80 (0.65–0.91)

 

VAE-augmented

0.88 (0.76–0.96)

0.87 (0.72–0.97)

0.89 (0.74–0.97)

0.88 (0.76–0.96)

0.87 (0.73–0.96)

Random Forest

Original

0.82 (0.69–0.93)

0.82 (0.66–0.94)

0.83 (0.69–0.94)

0.82 (0.68–0.92)

0.82 (0.67–0.93)

 

VAE-augmented

0.88 (0.75–0.96)

0.87 (0.71–0.97)

0.89 (0.74–0.97)

0.88 (0.75–0.96)

0.87 (0.73–0.96)

Logistic Regression

Original

0.79 (0.65–0.90)

0.82 (0.67–0.93)

0.78 (0.63–0.89)

0.79 (0.65–0.90)

0.77 (0.63–0.88)

 

VAE-augmented

0.85 (0.71–0.94)

0.87 (0.72–0.97)

0.83 (0.68–0.94)

0.85 (0.72–0.94)

0.84 (0.70–0.94)

DNN

Original

0.79 (0.64–0.90)

0.73 (0.55–0.87)

0.83 (0.68–0.93)

0.78 (0.63–0.89)

0.76 (0.60–0.88)

 

VAE-augmented

0.85 (0.71–0.94)

0.80 (0.63–0.92)

0.89 (0.74–0.97)

0.84 (0.70–0.93)

0.83 (0.68–0.93)

  1. GBM, Gradient Boosting Machine; VAE, variational autoencoder; DNN, Deep Neural Network; AUC-ROC, area under the receiver operating characteristic curve.
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