Adverserial network augmentation and tabular data for a new covid-19 diagnostics approach

This paper introduces a novel methodology for diagnosing COVID-19 leveraging Generative Adversarial Networks for Conditional Tabular data (GANCT). GANCT constructs a sophisticated conditional generative adversarial framework specialized for precise modeling of statistical distributions endemic to COVID-19 tabular datasets. Extensive experiments quantify the impact on predictive performance from augmenting the original data with synthetic GANCT samples. Results prove superior COVID-19 screening accuracy and ROC AUC across classifiers when supplementing real data with GANCT synthesizations. Moreover, we identified the optimal augmentation range that markedly improves performance while avoiding overfitting. The key contributions are: 1) A customized GAN architecture for COVID-19 tabular data synthesis, 2) Comprehensive evaluations demonstrating the benefits of GANCT augmentation, 3) Identification of optimal augmentation levels, and 4) Substantial improvements in COVID-19 prediction. GANCT can help overcome data scarcity to develop more reliable AI diagnostic systems for the pandemic.