Mitigating Data Scarcity in Healthcare through Wasserstein Generative Adversarial Network- A Case Study in Medical Application

Breast cancer represents a significant public health challenge, necessitating accurate predictive models for timely diagnosis and effective treatment. However, the scarcity and privacy constraints of medical datasets present substantial obstacles to developing robust predictive models. This study explores the application of Wasserstein Generative Adversarial Network (WGAN) to mitigate these challenges by generating synthetic breast cancer data. Using a comprehensive methodology that includes feature engineering, WGAN training, and model evaluation techniques, this research demonstrates the potential and effectiveness of integrating GAN-generated synthetic data into predictive modeling tasks. Evaluation metrics like Mean Squared Error (MSE) and Wasserstein distance (WD) are used to evaluate the quality of the generated data. Additionally, Random Forest is employed to evaluate model performance through accuracy scores and Area Under Curve (AUC) scores, which help assess the effectiveness of the predictive model for real, synthetic and combined data. The findings highlight the potential of WGAN to effectively enhance data availability and diversity, thereby improving predictive model performance in applications like breast cancer diagnosis and prognosis. In the future, ongoing advancements in GAN technology offer promising opportunities to refine data-driven methodologies in healthcare and advance patient care outcomes.