# GAN-Based Approaches for Generating Structured Data in the Medical Domain
## 3 Results
### Breast Cancer Wisconsin--BCW  
Figure 3 shows the accuracy of SVM and MLP classifiers trained on extended datasets of different sizes for two RS and RE samplings using the BCW dataset. For the Silver Standard, the classifier takes no generated data into account, and is trained only on subsets of the Train data of different sizes. In this case, the accuracy of the classifier improves as the size of the training data increases. This is more pronounced in the RE sampling, as the training size are gradually increased starting from the fixed initial size, while in the RS, the accuracy alternately increases and decreases as the data are randomly selected for each training data size. The randomness in RS leads in most cases to larger variations in the results, not only for the Silver Standard, but also for other models. The improved accuracy with the size of the training data, as observed for the Silver Standard, is to be expected because the more training data are fed into the classifiers, the higher the expected accuracy. The results of the GANs, however, exhibit a less accurate classification. Indeed, the SVM accuracy for GANs decreases clearly as more (extended) training data are provided to the classifier, and even the overall accuracy for the RE is lower compared to the same one in the RS. The latter is probably due to an overfitting of the GAN model to the (original) Train data when generating new data. This implies that the inclusion of the synthetically generated data from the original GANs might not readily improve the classifier performance. The reason would be that GANs learn only by recognizing patterns in the (original) Train data with no constraints on generating new data, which leads to a high variance in the generated data. To elucidate this, we performed a statistical measurement on the synthetic data for key features in a given dataset. Figure 4 shows the results for the mean perimeter of tumors in generated data from GAN variants over different training data sizes. It appears that GANs have a relatively high variance over the entire range of training sizes. Compared to GANs, the CGAN exhibits a significantly improved accuracy, though it is still below the Silver Standard. This shows that the introduction of additional information into CGAN, in this case, class labels, can dramatically increase the accuracy of the classifier. In RE sampling, this becomes even more evident as the learning performance gradually improves, while in RS, the curves fluctuate more due to random selection of the training data. In fact, a comparison of the results for the two classifiers shows a relatively similar behavior, apart from the random variations in RS. The results of CTGAN show an even higher accuracy on average, because CTGANs are designed specifically to consider tabular data. The performance of the classifiers for CTGANs is almost identical to that of the Silver  
Figure 4: Mean perimeter of tumors, a key feature in synthetic data from GAN variants over 10 different samples, versus the size of the training data (in percent) using the BCW. Standard, i.e., the model generates relatively the same statistics as the original Train data. Hence, the accuracy of the classifier also improves as the size of the training data increases, as for the Silver Standard. A similar pattern is observed for the CopulaGAN, with only lower accuracy and larger fluctuations. In this study, CopulaGANs are not provided with the statistical information required for their optimal performance, as we trained GAN variants in their standard format. The generated data could increase the accuracy only if the generative models were provided with more informative statistics than those already included in the Train data. We emphasize that further investigations are to be pursued to explore the best performance of the GAN models when provided with some additional information to exploit their capabilities.