Enhanced Deep Learning Approach For IoT Enabled Sensitive Clinical Data Analytics

Abstract

With the enormous upgrade of clinical sensors, it has acquired developing interest to investigate the Healthcare Internet of Things (H-IoT) because of its wide relevance for wellbeing of patient. Current medical services are providing only a limited significance with the emergence of a new strain Covid-19. People are dealing with an issue of unforeseen demise because of different ailment which is a direct result of absence of clinical consideration to the patients at perfect time. Hence in this situation, an IoT based smart healthcare checking framework is necessary during this pandemic. In this paper, an enhanced deep learning framework is proposed that efficiently deep belief network perform IoT-enabled clinical data analytics. The proposed framework is utilized to PIMA Indian physiological parameter dataset quantify to the actual boundaries like internal heat level, heart beat rate, and oxygen level checking with the assistance of sensors. The proposed framework involves the secured cloud infrastructure where the clinical sensor data are acquired and stored. For secured cloud storage and to provide an improved security, Improved Elliptic Curve Cryptography (IECC) algorithm utilized set up a transfer a medical records trust between the doctors and patients centralised repository trailed by enabling a typical session key for communication. The proposed solution automatically predicts any abnormalities exist in the patient and it is communicated to the medical experts for disease diagnosis and appropriate medical consultancy is provided for the patient through secured channel. The proposed framework is deployed in real-time environment for remote healthcare especially in rural area.