[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119351-en":3,"doc-seo-119351-105":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},119351,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","COVID-19 Prediction Using Supervised Machine Learning - Master of Engineering Report","Early diagnosis is essential to limit the spread of COVID-19, a contagious disease that has diversified into multiple variants and driven a global epidemic. This project applies data mining to a COVID-19 symptoms and presence dataset and uses supervised machine learning to predict infection status from observed symptoms. The study trains and evaluates multiple classifiers—Bayes Net, Simple Logistic, Bagging, SVM, and AdaBoost M1—using WEKA. Feature reduction relies on PCA, and models are tested with 5-fold and 10-fold cross-validation plus 66/34 and 34/66 splits. Performance is measured by accuracy, precision, recall, F-measure, and execution time, with Bagging achieving 99.3% accuracy.","COVID-19 Prediction Using Supervised Machine Learning  \nby  \nIrfan Ali  \nB.Sc., Sir Syed University of Engineering and Technology, 2015 A report submitted in partial fulfillment of the requirements for the degree of  \nMaster of Engineering  \nin the Department of Electrical and Computer Engineering  \n©Irfan Ali, 2023  \nUniversity of Victoria  \nAll rights reserved. This report may not be reproduced in whole or in part, by photocopy or other means, without the permission of the author  \nCOVID-19 Prediction Using Supervised Machine Learning  \nby  \nIrfan Ali  \nB.Sc., Sir Syed University of Engineering and Technology, 2015  \nSupervisory Committee  \nDr. T. Aaron Gulliver, Supervisor Department of Electrical and Computer Engineering  \nDr. Mihai Sima, Departmental Member Department of Electrical and Computer Engineering  \nAbstract  \nEarly diagnosis is important to stop the spread of illnesses that endanger human life. COVID-19 is a contagious disease that has mutated into multiple variants and created a global epidemic that requires immediate diagnosis. With the increase in COVID-19 cases, the amount of associated data grows every day, and data mining can be used to extract information from this data. In this project, a COVID-19 symptoms and presence dataset is used with several supervised machine learning algorithms to predict COVID-19 in the human body by examining the symptoms. The Bayes Net, Simple Logistic, Bagging, Support Vector Machine (SVM), and AdaBoost M1 classifiers are considered using the open-source Waikato Environment for Knowledge Analysis (WEKA) Machine Learning (ML) tool. Principal Component Analysis (PCA) is used to reduce the number of features in the dataset based on eigenvalues. Then the model is trained and tested using 5-fold cross-validation, 10-fold cross-validation, and 66/34 and 34/66 splits. The performance of the models is evaluated based on accuracy, precision, recall, F-measure, and execution time. The results obtained show that Bagging outperforms the other classifiers with an accuracy of 99.3% and an execution time of 0.10 s for a 66/34 split using 10 features.  \nContents  \nSupervisory Committee.................................................................................................................................ii  \nAbstract .........................................................................................................................................................iii  \nList of Figures ................................................................................................................................................v  \n[List of Tables.................................................................................................................................................](List of Tables.................................................................................................................................................vi)[vi](List of Tables.................................................................................................................................................vi)  \n[Glossary........................................................................................................................................................](Glossary........................................................................................................................................................vii)[vii](Glossary........................................................................................................................................................vii)  \n[Acknowledgment ........................................................................................................................................](Acknowledgment ........................................................................................................................................viii)[viii](Acknowledgment ..................................................","cbCaiijxhbcD22yy","https://ap.wps.com/l/cbCaiijxhbcD22yy","pdf",866580,1,48,"English","en",105,"# Supervisory Committee\n# Abstract\n# List of Figures\n# List of Tables\n# Glossary\n# Acknowledgment\n# Dedication\n# Chapter 1 Introduction\n## Motivation\n## Related Work\n## Report Outline\n# Chapter 2 Machine Learning\n## Types of Machine Learning Algorithms\n## Supervised Learning\n## Unsupervised Learning","[{\"question\":\"Which dataset and inputs are used to predict COVID-19?\",\"answer\":\"The project uses a COVID-19 symptoms and presence dataset, predicting infection by examining reported symptoms.\"},{\"question\":\"Which supervised learning models are evaluated in this report?\",\"answer\":\"Bayes Net, Simple Logistic, Bagging, Support Vector Machine (SVM), and AdaBoost M1 are evaluated using WEKA.\"},{\"question\":\"How are features reduced and how are models validated?\",\"answer\":\"Principal Component Analysis (PCA) reduces feature dimensionality. Models are trained and tested using 5-fold cross-validation, 10-fold cross-validation, and 66/34 and 34/66 train-test splits.\"}]","COVID-19 Prediction Using Supervised Machine Learning - Master of Engineering Report | PDF",1785723850,121,{"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},"covid-19-prediction-using-supervised-machine-learning-master-of-engineering-report","",{"@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/covid-19-prediction-using-supervised-machine-learning-master-of-engineering-report/119351/",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","2026-08-03",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},"Which dataset and inputs are used to predict COVID-19?","Question",{"text":75,"@type":76},"The project uses a COVID-19 symptoms and presence dataset, predicting infection by examining reported symptoms.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which supervised learning models are evaluated in this report?",{"text":80,"@type":76},"Bayes Net, Simple Logistic, Bagging, Support Vector Machine (SVM), and AdaBoost M1 are evaluated using WEKA.",{"name":82,"@type":73,"acceptedAnswer":83},"How are features reduced and how are models validated?",{"text":84,"@type":76},"Principal Component Analysis (PCA) reduces feature dimensionality. Models are trained and tested using 5-fold cross-validation, 10-fold cross-validation, and 66/34 and 34/66 train-test splits.","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":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]