Disease Prediction from Electronic Health Records Using Generative Adversarial Networks
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Electronic health records (EHRs) have contributed to the computerization of patient records so that it can be used not only for efficient and systematic medical services, but also for research on data science. In this paper, we compared disease prediction performance of generative adversarial networks (GANs) and conventional learning algorithms in combination with missing value prediction methods. As a result, the highest accuracy of 98.05 using stacked autoencoder as the missing value prediction method and auxiliary classifier GANs (AC-GANs) as the disease predicting method. Results show that the combination of stacked autoencoder and AC-GANs performs significantly greater than existing algorithms at the problem of disease prediction in which missing values and class imbalance exist.
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