IoT-Enabled Framework for Early Detection and Prediction of COVID-19 Suspects by Leveraging Machine Learning in Cloud

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, naïve 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.

A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media

“…In the research article [ 102 ], the authors introduced a real-time framework based on IoT aimed at detecting and forecasting deadly COVID-19 at its early stages by using symptomatic data and evaluating the nature of the virus. The introduced framework gathered real-time COVID-19 data from network sensors and IoT devices.…”

An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction

“…In addition, IoT could be applied in the medical field to guarantee people's safety and health while keeping expenses down. Mir et al [30] presented a real-time IoT-enabled architecture for COVID-19 diagnosis and prediction by gathering symptomatic indicators and better evaluating the virus's characteristics. By mining health information acquired in real-time detection from sensing devices and IoT objects, the framework was able to determine the existence of the COVID-19 virus.…”

A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine

“…Mir et al (2022) proposed a framework consisting of data collection center, data analytic center, diagnostic system, and cloud system (Mir et al, 2022). Through data collection and data analysis, the information would be stored into the cloud and be reused by healthcare professionals for further analysis.…”