[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123770-en":3,"doc-seo-123770-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123770,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Application of Machine Learning to Predict COVID-19 Spread via an Optimized BPSO Model","During the COVID-19 pandemic, reported case counts varied across countries and even between cities. This study develops an enhanced prediction model for COVID-19 samples in different regions of Saudi Arabia, comparing high-altitude Taif and sea-level Jeddah using two collected datasets. Binary particle swarm optimization (BPSO) performs feature selection with random forest, gradient boosting, and naive Bayes classifiers, evaluated through accuracy, precision, recall, F-measure, ROC curve, and training/testing scores. Results indicate gradient boosting achieves 94.6% accuracy on Taif data, while random forest reaches 95.5% on Jeddah data.","biomimetics  \nArticle  \nApplication of Machine Learning to Predict COVID-19 Spread via an Optimized BPSO Model  \nEman H. Alkhammash 1, Sara Ahmad Assiri 2, Dalal M. Nemenqani 3, Raad M. M. Althaqafi 3, Myriam Hadjouni 4, *, Faisal Saeed 5 and Ahmed M. Elshewey 6  \nCitation: Alkhammash, E.H.; Assiri, S.A.; Nemenqani, D.M.; Althaqafi, R.M.M.; Hadjouni, M.; Saeed, F.; Elshewey, A.M. Application of Machine Learning to Predict COVID-19 Spread via an Optimized BPSO Model. Biomimetics 2023, 8, 457 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)biomimetics8060457  \nAcademic Editor: Heming Jia  \nReceived: 26 August 2023  \nRevised: 21 September 2023  \nAccepted: 21 September 2023  \nPublished: 28 September 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia; [eman.kms@tu.edu.sa](eman.kms@tu.edu.sa)  \n2 Otolaryngology-Head and Neck Surgert Department, King Faisal Hospital, P.O. Box 11099, Taif 21944, Saudi Arabia; [saraassiriiii@gmail.com](saraassiriiii@gmail.com)  \n3 College of Medicine, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;  \n[d.nemenqani@tu.edu.sa](d.nemenqani@tu.edu.sa) (D.M.N.); [raad@tu.ed.sa](raad@tu.ed.sa) (R.M.M.A.)  \n4 Department of Computer Sciences, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia  \n5 DAAI Research Group, Department of Computing and Data Science, School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK; [faisal.saeed@bcu.ac.uk](faisal.saeed@bcu.ac.uk)  \n6 Faculty of Computers and Information, Computer Science Department, Suez University, Suez 43533, Egypt; [ahmed.elshewey@fci.suezuni.edu.eg](ahmed.elshewey@fci.suezuni.edu.eg)  \n* [Correspondence: mfhaojouni@pnu.edu.sa](Correspondence: mfhaojouni@pnu.edu.sa)  \nAbstract: During the pandemic of the coronavirus disease (COVID-19), statistics showed that the number of affected cases differed from one country to another and also from one city to another. Therefore, in this paper, we provide an enhanced model for predicting COVID-19 samples in different regions of Saudi Arabia (high-altitude and sea-level areas) . The model is developed using several stages and was successfully trained and tested using two datasets that were collected from Taif city (high-altitude area) and Jeddah city (sea-level area) in Saudi Arabia. Binary particle swarm optimization (BPSO) is used in this study for making feature selections using three different machine learning models, i.e., the random forest model, gradient boosting model, and naive Bayes model. A number of predicting evaluation metrics including accuracy, training score, testing score, F-measure, recall, precision, and receiver operating characteristic (ROC) curve were calculated to verify the performance of the three machine learning models on these datasets. The experimental results demonstrated that the gradient boosting model gives better results than the random forest and naive Bayes models with an accuracy of 94.6% using the Taif city dataset. For the dataset of Jeddah city, the results demonstrated that the random forest model outperforms the gradient boosting and naive Bayes models with an accuracy of 95.5% . The dataset of Jeddah city achieved better results than the dataset of Taif city in Saudi Arabia using the enhanced model for the term of accuracy.  \nKeywords: k-nearest neighbor; binary particle swarm optimization; random oversampling; random forest model; gradient boosting model; naive Bayes m","cbCaiapyq1t4cJRz","https://ap.wps.com/l/cbCaiapyq1t4cJRz","pdf",2335034,1,19,"English","en",105,"# Abstract\n# Keywords\n# 1. Introduction\n## Background on COVID-19 spread\n## Altitude-related differences in SARS-CoV-2 impact\n## Study motivation and limitations","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study addresses predicting COVID-19 spread across different regions, focusing on differences between high-altitude and sea-level areas in Saudi Arabia.\"},{\"question\":\"How does the model select features?\",\"answer\":\"It uses binary particle swarm optimization (BPSO) for feature selection, followed by training three machine learning models.\"},{\"question\":\"Which model performs best for each region dataset?\",\"answer\":\"For Taif (high-altitude), gradient boosting performs best with 94.6% accuracy; for Jeddah (sea-level), random forest performs best with 95.5% accuracy.\"}]","Application of Machine Learning to Predict COVID-19 Spread via an Optimized BPSO Model | PDF",1785818449,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"application-of-machine-learning-to-predict-covid-19-spread-via-an-optimized-bpso-model","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/application-of-machine-learning-to-predict-covid-19-spread-via-an-optimized-bpso-model/123770/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address?","Question",{"text":75,"@type":76},"The study addresses predicting COVID-19 spread across different regions, focusing on differences between high-altitude and sea-level areas in Saudi Arabia.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the model select features?",{"text":80,"@type":76},"It uses binary particle swarm optimization (BPSO) for feature selection, followed by training three machine learning models.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performs best for each region dataset?",{"text":84,"@type":76},"For Taif (high-altitude), gradient boosting performs best with 94.6% accuracy; for Jeddah (sea-level), random forest performs best with 95.5% accuracy.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]