CovMedCare: confluence of internet of things, blockchain and machine learning for remote monitoring system of pandemic patients

Considering the increasing number of global communicable diseases such as COVID-19, remote monitoring systems have become necessary in the healthcare industry. The paucity of a competent and secure remote patient monitoring system and a lack of an appropriate platform to disseminate the information about the patient’s condition to the concerned relatives and doctors was felt during the recent pandemic. Moreover, during COVID-19, a sudden deterioration in the patient’s condition was observed in the hospital. Motivated by these issues, we have proposed a secure and robust decentralized patient monitoring model called CovMedCare for monitoring COVID-19 inpatients utilizing the confluence of Machine Learning, the Internet of Things and Blockchain technologies. In the proposed model, sensor data from WBAN is integrated with the patient’s previous health records to predict the patient’s deteriorating health condition over a blockchain network for timely decisions with minimum delay. The accuracy of the proposed model is assessed on a dataset with five medical sensor attributes and fifteen previous health records attributes collected from a hospital with 40,000 samples. The extensive experimental results exhibit that the proposed system performs better than the existing ones and achieves an accuracy of 99% in identifying the patient whose health condition is deteriorating.