aspg-18-3979-1792025438 20261015005038 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2026 18 1 Hussein Ibrahim Hussein 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 Lateef Abd Zaid Qudr Department of Information Security, college of information technology, University of Babylon, Hillah, Iraq Weal Hasan Ali Almohammed Department of Computer Science, College of Computer Science and Information Technology, University of Kerbala, Karbala, Iraq 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 341 348 3979 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.180126 https://americaspg.com/journal/18/article/3979 https://americaspg.com/storage/51753647726.pdf