[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123858-en":3,"doc-seo-123858-105":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},123858,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Symptom-based Machine Learning Models for the Early Detection of COVID-19 - A Narrative Review","Despite widespread COVID-19 testing protocols, early detection still faces major obstacles that hinder preventing transmission and optimizing clinical outcomes. Limited testing capacity in resource-constrained settings and constraints of conventional diagnostic approaches make speed and efficiency critical. Symptom-based machine learning can learn from large datasets that combine patient-reported symptoms, clinical information, and related attributes. This narrative review summarizes symptoms-only models, detailing performance and limitations and comparing their effectiveness with image-based approaches while considering differences in datasets, methods, and evaluation metrics.","Symptom-based Machine Learning Models for the Early Detection ofCOVID-19: A Narrative Review  \nMoyosolu Akinloye  \nDepartment of Computing and  \nInformatics  \nBournemouth University  \nBournemouth, England, United  \nKingdom  \n[s5550338@bournemouth.ac.uk](s5550338@bournemouth.ac.uk)  \nAbstract—Despite the widespread testing protocols for COVID-19, there are still significant challenges in early detection of the disease, which is crucial for preventing its spread and optimizing patient outcomes. Owing to the limited testing capacity in resource-strapped settings and the limitations of the available traditional methods of testing, it has been established that a fast and efficient strategy is important to fully stop the virus. Machine learning models can analyze large datasets, incorporating patient-reported symptoms, clinical data, and medical imaging. Symptom-based detection methods have been developed to predict COVID-19, and they have shown promising results. In this paper, we provide an overview of the landscape of symptoms-only machine learning models for predicting COVID-19, including their performance and limitations. The review will also examine the performance of symptom-based models when compared to image-based models. Because different studies used varying datasets, methodologies, and performance metrics. Selecting the model that performs best relies on the context and objectives of the research. However, based on the results, we observed that ensemble classifier performed exceptionally well in predicting the occurrence of COVID-19 based on patient symptoms with the highest overall accuracy of 97.88%. Gradient Boosting Algorithm achieved an AUC (Area Under the Curve) of 0.90 and identified key features contributing to the decision-making process. Image-based models, as observed in the analyzed studies, have consistently demonstrated higher accuracy than symptom-based models, often reaching impressive levels ranging from 96.09% to as high as 99%.  \nKeywords-AUC (Area Under the Curve), Ensemble, CNN (Convolutional Neural Network), Fractional Multichannel Exponent Moments (FrMEMs), Area Under the Curve for the Receiver Characteristic (AUC ROC).  \nI. INTRODUCTION  \nCOVID-19, a virus caused by SARS-CoV-2, has had a global impact, affecting over 175 million individuals worldwide between December 2019 and June 2021[1] . Healthcare infrastructures in many countries, like that of the NHS (National Health Service) in the United Kingdom, have been under intense pressure due to the rising number of critical cases [2]. To alleviate this burden, prompt detection of COVID-19 cases is essential [1] . This paper provides an overview of the landscape of symptoms-only machine learning models for predicting COVID-19, encompassing their performance, limitations, and prospects.  \nWidespread testing for COVID-19 is crucial as it helps us understand the pattern of transmission of the virus, and if interventions are working [3] . To do this, the Percentage of positive COVID-19 tests, also known as Percent Positive (PP), out of all the tests done in a brief time should be  \nassessed. A high number of PP means the virus is spreading a lot among people, which also means that lots of tests need to be carried out. Alternatively, a low PP is an indicator that the virus is spreading less [4] . The WHO (World Health Organization) recommends targeting a PP below 5% as a measure to manage and curb the spread of the pandemic. When a lot of tests are carried out with lower PP, the efforts of interventions are amplified, and this slows down the spread of the pandemic. Testing is therefore important infighting the pandemic as it is the first step to detecting and diagnosing the virus.  \nAccording to Our World in Data, Mexico has performed just three tests per 100,000 people (about the seating capacity of the Los Angeles Memorial Coliseum) daily since the pandemic started. This is the second lowest testing rate among other countries that have been most impact","cbCaiaTgtVfOTHdB","https://ap.wps.com/l/cbCaiaTgtVfOTHdB","pdf",200266,1,6,"English","en",105,"# Introduction\n## Importance of early COVID-19 detection\n## Testing capacity and percent positive (PP)\n## Conventional testing methods and limitations\n## Need for fast, effective strategies","[{\"question\":\"Why is early detection of COVID-19 considered crucial even with widespread testing?\",\"answer\":\"Early detection is essential to prevent spread and to improve patient outcomes, but meaningful barriers still exist despite broad testing efforts.\"},{\"question\":\"What kinds of data do symptom-based machine learning models use for COVID-19 prediction?\",\"answer\":\"They use patient-reported symptoms alongside other available clinical data inputs, enabling models to learn from large datasets.\"},{\"question\":\"How do symptom-based models compare with image-based models according to the review?\",\"answer\":\"The review reports that image-based models in analyzed studies often achieve higher accuracy than symptom-based models, with symptom-only approaches showing promising but generally lower performance ranges.\"}]","Symptom-based Machine Learning Models for the Early Detection of COVID-19 - A Narrative Review | PDF",1785818921,15,{"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},"symptom-based-machine-learning-models-for-the-early-detection-of-covid-19-a-narrative-review","",{"@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/symptom-based-machine-learning-models-for-the-early-detection-of-covid-19-a-narrative-review/123858/",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},"Why is early detection of COVID-19 considered crucial even with widespread testing?","Question",{"text":75,"@type":76},"Early detection is essential to prevent spread and to improve patient outcomes, but meaningful barriers still exist despite broad testing efforts.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What kinds of data do symptom-based machine learning models use for COVID-19 prediction?",{"text":80,"@type":76},"They use patient-reported symptoms alongside other available clinical data inputs, enabling models to learn from large datasets.",{"name":82,"@type":73,"acceptedAnswer":83},"How do symptom-based models compare with image-based models according to the review?",{"text":84,"@type":76},"The review reports that image-based models in analyzed studies often achieve higher accuracy than symptom-based models, with symptom-only approaches showing promising but generally lower performance ranges.","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},[93,97,101,105,110,114,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"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"]