GANs and Fine-Tuning Through Transfer Learning for the Generation of Electronic Health Records on Chronic Kidney Diseases

This paper investigates synthetic data generation through Generative Adversarial Networks (GANs) and Transfer Learning (TL), focusing on Chronic Kidney Diseases (CKD). It analyzes whether GANs, particularly the medGAN and CorGAN architectures, can generate high-quality synthetic tabular data and how TL can enhance this process. The contributions include evaluating alternative medGAN and CorGAN setups, incorporating WGAN and WGAN-GP loss functions, and assessing how fine-tuning with TL impacts data realism and classifier performance. The models were pre-trained on a larger CKD dataset and fine-tuned on a smaller one, using consistent hyperparameters with reduced learning rates during fine-tuning. Experiments involved training Random Forest classifiers in various settings: using real data, synthetic data, and a combination of both, with and without TL. Metrics like dimension-wise probability and multiple training/testing scenarios are employed to assess the quality and utility of the generated data. The synthetic data, especially when generated using TL, improved classifier performance significantly in scenarios where training and testing datasets differed. Notably, F1-scores improved by up to 74.3 % when using TL-generated data. These findings support the use of GANs with TL as a powerful approach to overcome data limitations in healthcare research.