Automatic Intelligent Chronic Kidney Disease Detection in Healthcare 5.0

Health systems worldwide have an unprecedented opportunity to enhance healthcare service delivery due to the rapid development of emerging digital technologies. Many advancements have been made in the medical field, with deep learning proving particularly useful when applied to a large enough number of well-defined samples. Although, this aspect may make deep learning harder to implement in settings with limited-size datasets. In this study, we present a new method of chronic kidney disease detection (CKDD) by combining Generative Adversarial Networks (GAN) with Convolutional Neural Networks (CNN). Afterward, synthetic sample data was created using GAN, which enlarged the dataset. Subsequently, processing these synthetic samples, the CNN classifier was applied. According to experimental assessments, the suggested CKDD-GAN methodology accuracy is superior to without the GAN technique. Moreover, the proposed CKDD-GAN-based model outperformed with an accuracy of 98.10%. Even though standard synthetic data samples seemed to improve classification performance, GAN-based enhancements resulted in a 2.91% improvement. GAN implementations for detecting chronic kidney disease are highly beneficial since they also increase awareness about its possible uses in various other diseases.