Assessment of Machine Learning Techniques in IoT-Based Architecture for the Monitoring and Prediction of COVID-19

Abstract

From the end of 2019, the world has been facing the threat of COVID-19. It is predicted that, before herd immunity is achieved globally via vaccination, people around the world will have to tackle the COVID-19 pandemic using precautionary steps. This paper suggests a COVID-19 identification and control system that operates in real-time. The proposed system utilizes the Internet of Things (IoT) platform to capture users’ time-sensitive symptom information to detect potential cases of coronaviruses early on, to track the clinical measures adopted by survivors, and to gather and examine appropriate data to verify the existence of the virus. There are five key components in the framework: symptom data collection and uploading (via communication technology), a quarantine/isolation center, an information processing core (using artificial intelligent techniques), cloud computing, and visualization to healthcare doctors. This research utilizes eight machine/deep learning techniques—Neural Network, Decision Table, Support Vector Machine (SVM), Naive Bayes, OneR, K-Nearest Neighbor (K-NN), Dense Neural Network (DNN), and the Long Short-Term Memory technique—to detect coronavirus cases from time-sensitive information. A simulation was performed to verify the eight algorithms, after selecting the relevant symptoms, on real-world COVID-19 data values. The results showed that five of these eight algorithms obtained an accuracy of over 90%. Conclusively, it is shown that real-world symptomatic information would enable these three algorithms to identify potential COVID-19 cases effectively with enhanced accuracy. Additionally, the framework presents responses to treatment for COVID-19 patients.

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

“…The performance of the models is measured using root-mean-square error (RMSE) and R (correlation coefficient). The hybrid model of PNN and RBFNN performed better than all [ 50 ]. The authors have suggested the IoT-based identification and control system in real time.…”

Towards an ML-based semantic IoT for pandemic management: A survey of enabling technologies for COVID-19

“…Real-time detection of COVID-19 using IoT devices emerged during the pandemic. The proposed system in [169] , [174] uses an Internet of Things (IoT) framework to collect real-time symptom data from users. Its purpose is to detect suspected coronavirus cases early, monitor the treatment response of those who have already recovered from the virus, and learn more about its nature by collecting and analyzing relevant data.…”

IFCnCov: An IoT‐based smart diagnostic architecture for COVID‐19

“…Smart technology delivers real‐time, low‐cost, high‐quality healthcare. Aljumah 46 provided a real‐time COVID‐19 ID&C system that uses the IoT platform to gather users' time‐sensitive symptom information to find coronaviruses early, monitor the clinical actions taken by survivors, and collect and analyze the necessary data to demonstrate the virus's existence. Lydia et al 47 proposed an IoT‐enabled FDL‐COVID model.…”