Abstract:
COVID-19 is the repugnant but the most searched word since its outbreak in November 2019 across the globe. The world has to
battle with it until an effective solution is developed. Due to the advancement in mobile and sensor technology, it is possible to
come up with Internet of things-based healthcare systems. These novel healthcare systems can be proactive and preventive rather
than traditional reactive healthcare systems. This article proposes a real-time IoT-enabled framework for the detection and
prediction of COVID-19 suspects in early stages, by collecting symptomatic data and analyzing the nature of the virus in a better
manner. The framework computes the presence of COVID-19 virus by mining the health parameters collected in real time from
sensors and other IoT devices. The framework is comprised of four main components: user system or data collection center, data
analytic center, diagnostic system, and cloud system. To point out and detect the COVID-19 suspected in real time, this work
proposes the five machine learning techniques, namely support vector machine (SVM), decision tree, na¨ıve Bayes, logistic
regression, and neural network. In our proposed framework, the real and primary dataset collected from SKIMS, Srinagar, is used
to validate our work. The experiment on the primary dataset was conducted using different machine learning techniques on
selected symptoms. The efficiency of algorithms is calculated by computing the results of performance metrics such as accuracy,
precision, recall, F1 score, root-mean-square error, and area under the curve score. The employed machine learning techniques
have shown the accuracy of above 95% on the primary symptomatic data. Based on the experiment conducted, the proposed
framework would be effective in the early identification and prediction of COVID-19 suspect realizing the nature of the disease in
better way.
