[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124902-en":3,"doc-seo-124902-105":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},124902,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","COVID-19 From Symptoms to Prediction - A Statistical and Machine Learning Approach","During the COVID-19 pandemic, patient-data analysis became central to strengthening public health decisions. This study uses a dataset of over 10,000 anonymized records to predict COVID-19 patient age groups by combining statistical testing with multiple machine learning models. ANOVA and t-tests evaluate relationships among demographic and symptom variables, then classifiers such as Decision Tree, Naïve Bayes, KNN, Gradient Boosted Trees, SVM, and Random Forest are trained with preprocessing and tuned using ensemble strategies (bagging, boosting, stacking). Ensemble methods improve accuracy, with stacking using Random Forest as the meta-learner reaching 0.7054, supporting age-specific care guidance.","Citation:  \nFakieh, B and Saleem, F (2024) COVID-19 From Symptoms to Prediction: A Statistical and Machine Learning Approach. Computers in Biology and Medicine, 182 . pp. 1-15. ISSN 0010-4825 DOI: [https://doi.org/10.1016/j.compbiomed.2024.10921](https://doi.org/10.1016/j.compbiomed.2024.10921)1  \nLink to Leeds Beckett Repository record:  \n[https://eprints.leedsbeckett.ac.uk/id/eprint/11352/](https://eprints.leedsbeckett.ac.uk/id/eprint/11352/)  \nDocument Version:  \nArticle (Published Version)  \nCreative Commons: Attribution 4.0  \n© 2024 The Authors  \nThe aim of the Leeds Beckett Repository is to provide open access to our research, as required by funder policies and permitted by publishers and copyright law.  \nThe Leeds Beckett repository holds a wide range of publications, each of which has been checked for copyright and the relevant embargo period has been applied by the Research Services team.  \nWe operate on a standard take-down policy. If you are the author or publisher of an output and you would like it removed from the repository, please contact us and we will investigate on a case-by-case basis.  \nEach thesis in the repository has been cleared where necessary by the author for third party copyright. If you would like a thesis to be removed from the repository or believe there is an issue with copyright, please contact us on [openaccess@leedsbeckett.ac.uk](openaccess@leedsbeckett.ac.uk) and we will investigate on a case-by-case basis.  \nComputers in Biology and Medicine 182 (2024) 109211  \nContents lists available at ScienceDirect  \nComputers in Biology and Medicine  \njournal [homepage:](homepage: www.elsevier.com/locate/compbiomed)[ www.elsevier.com/locate/compbiomed](homepage: www.elsevier.com/locate/compbiomed)  \n| COVID-19 from symptoms to prediction: A statistical and machine learning approach\u003Cbr>Bahjat Fakieha, Farrukh Saleem b,*\u003Cbr>a Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia b School of Built Environment, Engineering, and Computing, Leeds Beckett University, Leeds, LS6 3QR, UK |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Machine learning Statistical analysis Ensemble algorithms COVID-19\u003Cbr>Public health informatics Predictive models |  | During the COVID-19 pandemic, the analysis of patient data has become a cornerstone for developing effective public health strategies. This study leverages a dataset comprising over 10,000 anonymized patient records from various leading medical institutions to predict COVID-19 patient age groups using a suite of statistical and machine learning techniques. Initially, extensive statistical tests including ANOVA and t-tests were utilized to assess relationships among demographic and symptomatic variables. The study then employed machine learning models such as Decision Tree, Naïve Bayes, KNN, Gradient Boosted Trees, Support Vector Machine, and Random Forest, with rigorous data preprocessing to enhance model accuracy. Further improvements were sought through ensemble methods; bagging, boosting, and stacking. Our findings indicate strong associations between key symptoms and patient age groups, with ensemble methods significantly enhancing model accuracy. Specifically, stacking applied with random forest as a meta leaner exhibited the highest accuracy (0.7054). In addition, the implementation of stacking techniques notably improved the performance of K-Nearest Neighbors (from 0.529 to 0.63) and Naïve Bayes (from 0.554 to 0.622) and demonstrated the most successful prediction method. The study aimed to understand the number of symptoms identified in COVID-19 patients and their association with different age groups. The results can assist doctors and higher authorities in improving treatment strategies. Additionally, several decision-making techniques can be applied during pandemic, tailored to specific age groups, such as resource allocation, me","cbCaidxqhnf4x1wv","https://ap.wps.com/l/cbCaidxqhnf4x1wv","pdf",5073680,1,16,"English","en",105,"# Introduction\n## Pandemic background and data-driven approaches\n## Study aim and research question\n# Methods\n## Dataset and preprocessing\n## Statistical tests (ANOVA, t-tests)\n## Machine learning models and ensemble strategies","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To develop a statistical and machine learning model that predicts COVID-19 patient age groups from available attributes and symptom information.\"},{\"question\":\"Which statistical methods are used before machine learning?\",\"answer\":\"The study applies ANOVA and t-tests to assess relationships between demographic and symptomatic variables.\"},{\"question\":\"How do ensemble methods affect prediction performance?\",\"answer\":\"Ensemble approaches such as bagging, boosting, and stacking significantly enhance accuracy compared with single models, with stacking achieving the highest reported accuracy (0.7054).\"}]","COVID-19 From Symptoms to Prediction - A Statistical and Machine Learning Approach | PDF",1785895307,40,{"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-from-symptoms-to-prediction-a-statistical-and-machine-learning-approach","",{"@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-from-symptoms-to-prediction-a-statistical-and-machine-learning-approach/124902/",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","2026-08-05",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 is the main objective of the study?","Question",{"text":75,"@type":76},"To develop a statistical and machine learning model that predicts COVID-19 patient age groups from available attributes and symptom information.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which statistical methods are used before machine learning?",{"text":80,"@type":76},"The study applies ANOVA and t-tests to assess relationships between demographic and symptomatic variables.",{"name":82,"@type":73,"acceptedAnswer":83},"How do ensemble methods affect prediction performance?",{"text":84,"@type":76},"Ensemble approaches such as bagging, boosting, and stacking significantly enhance accuracy compared with single models, with stacking achieving the highest reported accuracy (0.7054).","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]