https://socictopen.socict.org/files/original/21fae08aad9150d4ec99584c665007fe.pdf a0c1363f274db93bd444e7f3d1fdaaf9 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Novel Ensemble-based Classifier for Detecting the COVID-19 Disease for Infected Patients. Creator An entity primarily responsible for making the resource Prabh Deep Singh, Rajbir Kaur, Kiran Deep Singh, Gaurav Dhiman Description An account of the resource The recently discovered coronavirus, SARS-CoV-2, which was detected in Wuhan, China, has spread worldwide and is still being studied at the end of 2019. Detection of COVID-19 at an early stage is essential to provide adequate healthcare to affected patients and protect the uninfected community. This paper aims to design and develop a novel ensemble-based classifier to predict COVID-19 cases at a very early stage so that appropriate action can be taken by patients, doctors, health organizations, and the government. In this paper, a synthetic dataset of COVID-19 is generated by a dataset generation algorithm. A novel ensemble-based classifier of machine learning is employed on the COVID-19 dataset to predict the disease. A convex hull-based approach is also applied to the data to improve the proposed novel, ensemble-based classifier's accuracy and speed. The model is designed and developed through the python programming language and compares with the most popular classifier, i.e., Decision Tree, ID3, and support vector machine. The results indicate that the proposed novel classifier provides a more significant precision, kappa static, root means a square error, recall, F-measure, and accuracy. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource coronavirus, covid-19, machine learning, artificial intelligence, Quality of service, ensemble classifier Identifier An unambiguous reference to the resource within a given context 10.1007/s10796-021-10132-w Source A related resource from which the described resource is derived Information systems frontiers : a journal of research and innovation