International Journal of Science, Engineering and TechnologyAuthors: Pooja Dhankade, Dr. Pravin Kumar Malviya
Abstract: The quick data driven technology adoption of the health care sector has extinguished the notion that we require large volumes and superiorities of medical data to drive the machine learning, predictive analytics and clinical decision support systems. Simultaneously access to actual patient data is much more of an issue which we possess because of privacy laws, ethical concerns and organizational concerns. Researchers and practitioners are therefore the ones that are struggling immensely in development and validation of health care models that utilize real world data. Synthetically generated medical data has put forth as a workable and private solution to these issues. Here we put forth a model which we have named Generative Adversarial Networks (GANs) for the generation of artificial medical records out of structured and systematic health care data. We have designed the framework around practical aspects of the system, pre-processing of the data set, GAN architecture implementation, training protocols, and also the quantitative assessment. We used real medical follow up data in CSV files to train the GAN model which in turn generated synthetic data to very much like that of the real data set in terms of its statistical properties and the relationship between variables. We reported very good correlation between real and synthetic data distributions using primary clinical variables which in turn proved the put forth method’s performance. Also in general the system we present is a scalable solution to improve health care analytics data collection and at the same time it protects patient privacy which in turn is very beneficial for health care research and machine learning applications.
DOI: http://doi.org/10.5281/zenodo.20796896
