TGAN and CTGAN: A Comparative Analysis for Augmenting COVID 19 Tabular Data

Eman Kamal Al-Bwana, Mohammad Alauthman, Ikbel Sayahi, Mohamed Ali Mahjoub

2025

Abstract

The discovery of COVID-19 has drawn attention to the need for relatively fast and accurate diagnostic solutions for clinical applications. However, the creation of high-quality AI systems is often hampered by the lack of sufficient amounts of similar reference datasets. Therefore, GANs have emerged as useful tools to address this challenge through synthetic data. Building on our previous work on conditional tabular GANs (CTGANs), this study proposes a novel TGAN architecture for augmenting tabular COVID-19 data. To evaluate the performance of TGAN-based augmentation, we conduct extensive tests to compare its performance with CTGAN while using several machine learning classifiers for prediction. The results on evaluation criteria such as precision, accuracy, recall, F-measure, and ROC AUC show that the proposed TGAN outperforms CTGAN. It is worth noting that the logistic regression classifier achieves a test accuracy of 0.746, precision of 0.734, and recall of 0.928 when trained on the provided TGAN-augmented dataset, which is higher than those on the original and CTGAN-augmented datasets. In addition, the augmentation range was optimal at 100% as we balance performance and the risk of overfitting. The developed TGAN method provides an effective tool for generating synthetic samples that provide a description of the data distribution and improve COVID-19 diagnostic models. This study demonstrates the feasibility of TGAN-based data augmentation in overcoming the data shortage issues by creating efficient and reliable AI systems to support clinical decisions regarding upcoming pandemics.

Paper Citation

in Harvard Style

Al-Bwana E., Alauthman M., Sayahi I. and Mahjoub M. (2025). TGAN and CTGAN: A Comparative Analysis for Augmenting COVID 19 Tabular Data. In Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-749-8, SciTePress, pages 387-393. DOI: 10.5220/0013483200003929

in Bibtex Style

@conference{iceis25,
author={Eman Al-Bwana and Mohammad Alauthman and Ikbel Sayahi and Mohamed Mahjoub},
title={TGAN and CTGAN: A Comparative Analysis for Augmenting COVID 19 Tabular Data},
booktitle={Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2025},
pages={387-393},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013483200003929},
isbn={978-989-758-749-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - TGAN and CTGAN: A Comparative Analysis for Augmenting COVID 19 Tabular Data
SN - 978-989-758-749-8
AU - Al-Bwana E.
AU - Alauthman M.
AU - Sayahi I.
AU - Mahjoub M.
PY - 2025
SP - 387
EP - 393
DO - 10.5220/0013483200003929
PB - SciTePress