Utility of GAN generated synthetic data for cardiovascular diseases mortality prediction: an experimental study

Electronic Health Records (EHRs) are invaluable sources of information for healthcare research and decision-making. However, laws protecting patient privacy restrict the sharing of real EHR data thus impeding the development of advanced AI based healthcare technology which require large volumes of quality data. To bridge this gap, synthetic data (SD) has emerged as a potential privacy-preserving alternative to real data. While SD can serve as a proxy to real data in many practical scenarios, its true potential is still unexploited because of insufficient empirical evidence Nevertheless lack of sufficient empirical evidence supporting its efficacy has led to skepticism and decreased trust in SD among the stakeholders. This research article presents the result of extensive experimentation with SD in prediction of Cardiovascular Disease (CVD) mortality.

Generative adversarial networks (GANs) are a popular choice for generating SD, especially in the medical domain. We perform two controlled experiments to evaluate the effectiveness of the state-of-the-art GAN models for CVD SD generation, and to study the impact of increasing data-dimensionality upon the utility of generated SD.

The results demonstrate that GAN-generated SD performs well in predicting CVD, with comparable accuracy to that of real data, and highlights the potential of SD for disease prediction.

We believe that our results will leverage better trust on practical use cases of SD among medical practitioners and user stakeholders for applications such as decision support systems, health monitoring and planning, and mobile health systems.