Vaccines 2022, 10, 366. https://doi.org/10.3390/vaccines10030366 www.mdpi.com/journal/vaccines Article Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors Ma’mon M. Hatmal 1, * ,† , Mohammad A. I. Al-Hatamleh 2,† , Amin N. Olaimat 3 , Rohimah Mohamud 2 , Mirna Fawaz 4 , Elham T. Kateeb 5 , Omar K. Alkhairy 6,7,8 , Reema Tayyem 9 , Mohamed Lounis 10 , Marwan Al-Raeei 11 , Rasheed K. Dana 12 , Hamzeh J. Al-Ameer 13 , Mutasem O. Taha 14 and Khalid M. Bindayna 15, * 1 Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan 2 Department of Immunology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia; alhatamleh@student.usm.my (M.A.I.A.-H.); rohimahm@usm.my (R.M.) 3 Department of Clinical Nutrition and Dietetics, Faculty of Applied Medical Sciences, The Hashemite Uni- versity, P.O. Box 330127, Zarqa 13133, Jordan; aminolaimat@hu.edu.jo 4 Nursing Department, Faculty of Health Sciences, Beirut Arab University, Beirut 1105, Lebanon; mirna.fawaz@bau.edu.lb 5 Oral Health Research and Promotion Unit, Faculty of Dentistry, Al-Quds University, Jerusalem 51000, Palestine; ekateeb@staff.alquds.edu 6 Department of Pathology and Laboratory Medicine, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, P.O. Box 22490, Riyadh 11426, Saudi Arabia; alkhairyom1@ngha.med.sa 7 King Saud bin Abdulaziz University for Health Sciences, P.O. Box 3660, Riyadh 11481, Saudi Arabia 8 King Abdullah International Medical Research Center (KAIMRC), P.O. Box 3660, Riyadh 11481, Saudi Arabia 9 Department of Human Nutrition, College of Health Sciences, QU Health, Qatar University, Doha P.O. Box 2713, Qatar; reema.tayyem@qu.edu.qa 10 Department of Agro-Veterinary Science, Faculty of Natural and Life Sciences, University of Ziane Achour, BP 3117, Djelfa 17000, Algeria; lounisvet@gmail.com 11 Faculty of Sciences, Damascus University, Damascus P.O. Box 30621, Syria; mn41@live.com 12 Faculty of Medicine, Mansoura University, Mansoura, Dakahlia 35516, Egypt; rasheed.khd95@gmail.com 13 Department of Biology and Biotechnology, Faculty of Science, American University of Madaba, P.O. Box 99, Madaba 17110, Jordan; h.alameer@aum.edu.jo 14 Department of Pharmaceutical Sciences, Faculty of Pharmacy, The University of Jordan, Amman 11942, Jordan; mutasem@ju.edu.jo 15 Department of Microbiology, Immunology and Infectious Diseases, College of Medicine and Medical Sciences, Arabian Gulf University, Manama 329, Bahrain * Correspondence: mamon@hu.edu.jo (M.M.H.); bindayna@agu.edu.bh (K.M.B.) † These authors contributed equally to this work. Abstract: Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19) has imposed huge challenges on the healthcare facilities, and impacted every aspect of life. This has led to the development of several vaccines against COVID-19 within one year. This study aimed to assess the attitudes and the side effects among Arab communities after receiving a COVID-19 vac- cine and use of machine learning (ML) tools to predict post-vaccination side effects based on pre- disposing factors. Methods: An online-based multinational survey was carried out via social media platforms from June 14 to 31 August 2021, targeting individuals who received at least one dose of a COVID-19 vaccine from 22 Arab countries. Descriptive statistics, correlation, and chi-square tests were used to analyze the data. Moreover, extensive ML tools were utilized to predict 30 post vac- cination adverse effects and their severity based on 15 predisposing factors. The importance of dis- tinct predisposing factors in predicting particular side effects was determined using global feature Citation: Hatmal, M.M.; Al-Hatamleh, M.A.I.; Olaimat, A.N.; Mohamud, R.; Fawaz, M.; Kateeb, E.T.; Alkhairy, O.K.; Tayyem, R.; Lounis, M.; Al-Raeei, M.; et al. Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors. Vaccines 2022, 10, 366. https://doi.org/10.3390/vac- cines10030366 Academic Editors: Scott Anthony, François Meurens and Ralph A. Tripp Received: 20 January 2022 Accepted: 24 February 2022 Published: 26 February 2022 Publisher’s Note: MDPI stays neu- tral with regard to jurisdictional claims in published maps and institu- tional affiliations. Copyright: © 2022 by the authors. Li- censee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and con- ditions of the Creative Commons At- tribution (CC BY) license (https://cre- ativecommons.org/licenses/by/4.0/).