Using CTGAN to Generate Synthetic Tabular Data

Introduction & Method

In healthcare, data is the driving force behind research, diagnostics, and clinical decision-making. Machine learning (ML) and artificial intelligence (AI) models are becoming increasingly vital for predicting disease outcomes, optimizing treatment plans, and improving patient care. However, a common challenge that researchers face is the availability of high-quality, balanced datasets. In many scenarios, datasets are often imbalanced, meaning that one class (like healthy patients) vastly outnumbers another (such as patients with a rare disease).

In these datasets, one class (usually the condition of interest, such as a rare disease or adverse treatment outcome) is vastly underrepresented compared to the other classes (like healthy patients or successful treatments). This imbalance can significantly impact the performance of machine learning (ML) and artificial intelligence (AI) models.

This imbalance can significantly affect the performance of ML models, leading to biased predictions that favor the majority class. In critical fields like healthcare, where identifying minority classes (such as detecting cancer or rare genetic disorders) can have life-or-death implications, this bias is particularly concerning.

To overcome these challenges, researchers have turned to synthetic data generation techniques to augment existing datasets. Among the various approaches, the Conditional Tabular Generative Adversarial Network (CTGAN) has gained attention for its ability to generate realistic synthetic data, even when dealing with complex, imbalanced datasets.

This blog explores how CTGAN can be leveraged to generate synthetic data in healthcare settings, addressing the problem of class imbalance and ultimately improving the performance of ML models. By doing so, researchers and data scientists can develop more accurate, reliable, and fair models that are better suited for real-world applications in medicine and clinical research.