[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116987-en":3,"doc-seo-116987-105":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},116987,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Optimizing HCV Disease Prediction in Egypt - The hyOPTGB Framework","The study addresses hepatitis C virus (HCV) infection in Egypt, where HCV prevalence is among the highest worldwide and is associated with injection drug use, inadequate medical sterilization, and limited public awareness. A hyOPTGB model is proposed to predict HCV using an optimized gradient boosting classifier. Hyperparameters are tuned via the OPTUNA framework, with Min-Max normalization and a forward selection wrapped method to identify key features. Using 1385 instances and 29 features from the UCI repository, hyOPTGB achieves 95.3% accuracy and outperforms multiple baseline models across metrics including recall, precision, and F1-score.","UC Berkeley  \nUC Berkeley Previously Published Works  \nTitle  \nOptimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework  \nPermalink  \n[https://escholarship.org/uc/item/0203z289](https://escholarship.org/uc/item/0203z289)  \nJournal  \nDiagnostics, 13(22)  \nISSN  \n2075-4418  \nAuthors  \nElshewey, Ahmed M  \nShams, Mahmoud Y Tawfeek, Sayed Met al.  \nPublication Date  \n2023  \nDOI  \n10.3390/diagnostics13223439  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \n diagnostics  \nArticle  \nOptimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework  \nAhmed M. Elshewey 1, *, Mahmoud Y. Shams 2, Sayed M. Tawfeek 3, Amal H. Alharbi 4, Abdelhameed Ibrahim 5, Abdelaziz A. Abdelhamid 6,7, Marwa M. Eid 3,8,*, Nima Khodadadi 9, Laith Abualigah 10,11,12,13,14,15,16, Doaa Sami Khafaga 4 and Zahraa Tarek 17  \nCitation: Elshewey, A.M.; Shams, M.Y.; Tawfeek, S.M.; Alharbi, A.H.; Ibrahim, A.; Abdelhamid, A.A.; Eid, M.M.; Khodadadi, N.; Abualigah, L.; Khafaga, D.S.; et al. Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework. Diagnostics 2023, 13, 3439. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/diagnostics13223439](10.3390/diagnostics13223439)  \nAcademic Editor: Kathiravan Srinivasan  \nReceived: 22 September 2023  \nRevised: 4 November 2023  \nAccepted: 8 November 2023  \nPublished: 13 November 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 Computer Science Department, Faculty of Computers and Information, Suez University, Suez 43533, Egypt  \n2 Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt  \n3 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt  \n4 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia  \n5 Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt  \n6 Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt  \n7 Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia  \n8 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt  \n9 Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL 33146, USA; [nima.khodadadi@miami.edu](nima.khodadadi@miami.edu)  \n10 Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan  \n11 Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon  \n12 Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan  \n13 MEU Research Unit, Middle East University, Amman 11831, Jordan  \n14 Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan  \n15 School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Malaysia  \n16 School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya 27500, Malaysia  \n17 Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35561, Egypt  \n* Correspondence: [ahmed.elshewey@fci.suezuni.edu.e","cbCaikZ70rnJMT8i","https://ap.wps.com/l/cbCaikZ70rnJMT8i","pdf",5180016,1,25,"English","en",105,"# Introduction\n# Proposed Method: hyOPTGB Framework\n## Data preprocessing and feature selection\n## Hyperparameter optimization with OPTUNA\n# Experimental Setup and Models\n## Compared machine learning baselines\n# Results and Evaluation\n## Accuracy, recall, precision, and F1-score\n# Conclusion","[{\"question\":\"What problem does the study address in Egypt?\",\"answer\":\"The study targets hepatitis C virus (HCV) prediction in Egypt, where HCV infection rates are among the highest worldwide.\"},{\"question\":\"How does the hyOPTGB framework improve HCV prediction accuracy?\",\"answer\":\"It uses an optimized gradient boosting classifier, tunes hyperparameters with OPTUNA, applies Min-Max normalization, and employs forward selection to choose essential features.\"},{\"question\":\"What dataset and evaluation metrics are used to assess performance?\",\"answer\":\"The experiments use a dataset with 1385 instances and 29 features from the UCI repository, and evaluate performance using accuracy, recall, precision, and F1-score.\"}]","Optimizing HCV Disease Prediction in Egypt - 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