Generative AI mitigates representation bias and improves model fairness through synthetic health data
Fig 3
Distribution plots[0mm][-3mm]AQ1[4mm][-3mm]AQ2 of each variable, overlaying real and synthetic data for acute hypotension dataset.
Distribution of variables related to blood pressure (MAP, diastolic and systolic) is captured well by our method in comparison to WGAN-GP* and SMOTE. CA-GAN performs better also for categorical variables, while all the three methods struggle with variables with long tail, non-normal distributions. Top panel: CA-GAN. Middle panel: WGAN-GP. Bottom panel: SMOTE