Generative AI for Synthetic Medical Data Generation and Privacy Protection

Healthcare data is essential for developing accurate machine learning models, but privacy concerns often limit its accessibility. This study proposes a Generative AI-based approach for generating synthetic healthcare data that preserves the statistical characteristics of real patient data while protecting sensitive information. The methodology involves data preprocessing, synthetic data generation using a generative model, and evaluation through statistical similarity and machine learning performance metrics. The generated synthetic dataset is compared with the original dataset to assess data quality, utility, and privacy preservation. The results demonstrate that synthetic data can effectively support medical analytics without exposing confidential patient information. This approach has the potential to facilitate secure data sharing, improve AI model development, and promote privacy-preserving healthcare research.

 

Keywords—Synthetic Healthcare Data, Generative Artificial Intelligence (Generative AI), Conditional Tabular Generative Adversarial Network (CTGAN), Privacy Preservation, Healthcare Analytics, Machine Learning, Synthetic Data Generation, Electronic Health Records (EHRs), Data Security, Medical Data Analytics.

 

—Synthetic Healthcare Data Generative Artificial Intelligence (Generative AI) Conditional Tabular Generative Adversarial Network (CTGAN) Privacy Preservation Healthcare Analytics Machine Learning Synthetic Data Generation Electronic Health Records (EHRs) Data Security Medical Data Analytics.