A Survey on Machine Learning and Internet of Things for COVID-19

Abstract

COVID-19 has affected many areas of life worldwide, such as the economy, education, security, social life, and health. In this work, we survey research papers on machine learning, the Internet of things (IoT), medical imaging, and software applications to prevent, diagnose, reduce, and manage COVID-19. Artificial intelligence is an important research area to solve problems in emergent domains from homeland security to biomedical engineering. Artificial intelligence subdomains such as image processing, data mining, networks, graph theory, natural language processing, and computer vision are frequently applied for COVID-19 data. IoT sensors collect data from patients and elderly people in their homes, hospital rooms, or elsewhere for early prediction and monitoring. The collected data are used in machine learning algorithms such as decision tree, naïve Bayes classifier, neural network, and k-means algorithms for classification and clustering. Computed tomography is also commonly used to determine the presence of any COVID-19 infection or damage in patients' lungs. Image segmentation, object detection, and object tracking are used to extract features from medical images. Experimental results of these surveyed papers demonstrate that these approaches are promising for predicting, diagnosing, and managing COVID-19.

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