Synthetic Data Generation

Project Overview

A healthcare AI startup needed a large dataset to train their diagnostic models but was constrained by strict patient privacy regulations (like HIPAA).

The Challenge

Acquiring and anonymizing real patient data is a slow, expensive, and legally complex process. The lack of data was a major bottleneck for their research and development.

My Solution

I developed a Generative Adversarial Network (GAN) to create a synthetic dataset of realistic, but entirely artificial, patient records.

Statistical Accuracy: The GAN was trained on a small, anonymized seed dataset to learn the statistical properties and correlations of real medical data.

Privacy Preservation: The generated data is mathematically guaranteed to contain no personally identifiable information, making it safe for open research.

Data Augmentation: The system can generate vast amounts of data, allowing the startup to train more robust and accurate machine learning models.

This approach unlocked their ability to innovate rapidly while upholding the highest standards of patient privacy.