BaRCODe: A Blockchain-based Framework for Remote COVID Detection for Healthcare 5.0

ABSTRACT

The emergence of wearable technology for assessing health data has revolutionized the health sector. Consequently, medical practitioners can now virtually examine the patient's health and provide immediate medications. However, when the security of this equipment is considered, there is a grave hazard. The data is delivered across an open channel, i.e., the internet, from the patient's device to the doctor, and it may be tampered with by intruders. Insurance firms keep a record of their patient's health and subsequently offer appropriate treatments. In case of data tampering, the insurer will consider the conduct fraudulent. As a result, ensuring the system's integrity and granting access only to authorized stakeholders becomes critical. Blockchain has surpassed conventional technologies in terms of guaranteeing security for information held. Motivated by these, this paper has developed a novel approach that uses blockchain technology to transmit a patient's health information to a medical expert. Machine Learning (ML) techniques, K-Nearest Neighbours (KNN), and Logistic Regression (LR) is used to categorize Coronavirus Disease (COVID) positive users and malevolent wearable devices, respectively. The performance of the proposed model is evaluated considering parameters such as accuracy, precision, recall, and F1 score. The proposed model achieves an accuracy of 98.15% for COVID positive detection and 96.78% for malevolent user detection. © 2022 IEEE.