Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3979 2019 2019 Department of computer engineering techniques, Alsafwa university college, Almamalie str Karbala, Iraq; Department of Information Security, college of information technology, University of Babylon, Hillah, Iraq Hussein Hussein Department of computer engineering techniques, Alsafwa university college, Almamalie str Karbala, Iraq Lateef Abd Zaid Qudr Department of Computer Science, College of Computer Science and Information Technology, University of Kerbala, Karbala, Iraq Weal Hasan Ali Almohammed Mammals are susceptible to the lethal disease called coronavirus. This virus often infects humans through the aerial precipitation of any fluid released from the bodily part of the affected entity. This viral variant is deadlier than other sudden viruses. Given the ongoing thread which COVID-19 on health systems in the worldwide, there is a rising interest in development a mechanism that effective in terms of cost and classification. A mechanism for categorizing and scrutinizing the estimations derived from this virus' symptoms is proposed in this paper. The precision of various machine-learning classifiers is calculated in this study in order to determine the optimal classifier for COVID-19 identification. Because the COVID-19 dataset has the greatest precision of 100%, it was classified using AdaBoost and Bagging. Additionally, precision, recall, and F-score measures together with the ROC were deployed for evaluating detection performance to ensure the approach is capable and successful. 2026 2026 341 348 10.54216/JISIoT.180126 https://www.americaspg.com/articleinfo/18/show/3979