CovMedCare: Confluence of Internet of Things, Blockchain and Machine Learning for Remote Monitoring System of Pandemic Patients

Abstract

Pandemics like Covid-19 necessitate the need for continuous remote monitoring of patients. The scarcity of an efficient and secure remote monitoring system for patients led to surge in work pressure for medical staff in hospitals globally. In this paper, we propose a secure and robust decentralized patient monitoring model called CovMedCare for monitoring the Covid-19 in-patients utilizing the confluence the Machine Learning, Internet of Things and Blockchain technologies. In the proposed model, sensor data from WBAN is integrated with previous health records of the patients to predict deteriorating health condition of the patient over a blockchain network. The accuracy of the proposed model is assessed on a dataset with five medical sensor attributes and twelve previous health records attributes. The experimental results exhibit that CovMedCare achieves an accuracy of 99% in identifying the patient whose health condition is deteriorating, which is higher than that of other traditional machine learning models.