Synthetic Data Generation for Healthcare: Exploring Generative Adversarial Networks Variants for Medical Tabular Data

Abstract Recently, the medical and healthcare fields have experienced significant improvements. However, the restrictions of ethical constraints, privacy regulations, and preservation for sharing sensitive personal information limit access to real patient data. Synthetic datasets with generative models are considered one of the most reliable solutions that meet strict data protection requirements. Synthetic data are created in a controlled environment but possess the same statistical and structural properties as real data. In this work, we generate synthetic data using six variations of generative adversarial networks (GANs): GAN, CGAN, CTGAN, CRAMER GAN, DRAGAN, and WGAN. We explore the efficacy of synthetic data in three distinct healthcare datasets: Breast Cancer Wisconsin (Diagnostic), Lung Cancer Patient, and Fetal Cardiotocography CTG. To evaluate the performance of these generated datasets in classification tasks, we employ two diverse classifiers, namely XGBoost and SVM. In addition, we employ correlation and statistical analyses to scrutinise GAN models, identifying optimal variants for specific data generation tasks. Our experimental framework encompasses the examination of original (real), synthetic, and hybrid (original and synthetic) datasets. Our findings highlight a notable improvement in classification accuracy when using advanced GAN models such as CGAN and CTGAN to generate tabular data. This research sheds light on the potential of synthetic data in bolstering data privacy while facilitating meaningful insights in the realm of healthcare analytics.