Machine learning-based D2D communication for a cloud-secure e-health system and data analysis by feature selection with classification

Abstract

Abstract Numerous aspects of healthcare have been altered by cloud-based computing. Scalability of required service as well as ability to upscale or downsize data storage, as well as the collaboration between AI and machine learning, are main benefits of cloud computing in healthcare. Current paper looked at a number of different research studies to find out how intelligent techniques can be used in health systems. The main focus was on security and privacy concerns with the current technologies. This study proposes a novel method for cloud service device-to-device communication using feature selection and classification for data analysis in an e-health system. Through a comprehensive requirement analysis as well as user study, the purpose of this research is to investigate viability of incorporating cloud as well as distributed computing into e-healthcare. After that, the smart healthcare system and conventional database-centric healthcare methods will be compared, and a prototype system will be created as well as put into use based on results. Convolutional adversarial neural networks with transfer perceptron are used to analyze the cloud-based e-health data that has been collected. Proposed technique attained training accuracy 98%, validation accuracy 93%, PSNR 66%, MSE 68%, precision 72%, QoS 63%, Latency 58%.