[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119250-en":3,"doc-seo-119250-105":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":20,"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},119250,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","A Machine Learning Approach to Identifying Risk Factors for Long COVID-19","Long-term sequelae after coronavirus disease 2019 (COVID-19) infection are widespread and can lead to disabling outcomes, creating clinical and public health challenges. The study targets key predictors of Long COVID-19 by leveraging machine learning to build a predictive algorithm. Longitudinal cohort data from the UK “Understanding Society: COVID-19 Study” (n=601) were analyzed using a random forest classifier. Results show strong sensitivity (97.4%) and moderate specificity (65.4%), with risk linked to earlier acute infection timing during the pandemic, more weekly working hours, older age, and financial insecurity.","Bond University Research Repository  \nA Machine Learning Approach to Identifying Risk Factors for Long COVID-19  \nMachado, Rhea; Dodhy, Reshen Soorinarain; Sehgal, Atharve; Rattigan, Kate; Lalwani, Aparna; Waynforth, David  \nPublished in: Algorithms  \nDOI:  \n10.3390/a17110485  \nLicence:  \nCC BY  \nLink to output in Bond University research repository.  \nRecommended citation(APA):  \nMachado, R. , Dodhy, R. S. , Sehgal, A. , Rattigan, K. , Lalwani, A. , & Waynforth, D. (2024) . A Machine Learning Approach to Identifying Risk Factors for Long COVID-19 . Algorithms, 17(11), 1-16.  \n[https://doi.org/10.3390/a17110485](https://doi.org/10.3390/a17110485)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nFor more information, or if you believe that this document breaches copyright, please contact the Bond University research repository coordinator.  \nDownload date: 17 Nov 2024  \n algorithms  \nArticle  \nA Machine Learning Approach to Identifying Risk Factors for Long COVID-19  \nRhea Machado, Reshen Soorinarain Dodhy, Atharve Sehgal, Kate Rattigan, Aparna Lalwani and David Waynforth *  \nCitation: Machado, R.; Soorinarain Dodhy, R.; Sehgal, A.; Rattigan, K.; Lalwani, A.; Waynforth, D. A Machine Learning Approach to Identifying Risk Factors for Long COVID-19 . Algorithms 2024, 17, 485. [https://](https://)[ ](https://)[doi.org/10.3390/a17110485](doi.org/10.3390/a17110485)  \nAcademic Editor: Frank Werner  \nReceived: 30 September 2024  \nRevised: 18 October 2024  \nAccepted: 25 October 2024  \nPublished: 28 October 2024  \nCopyright: © 2024 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/)) .  \nSchool of Medicine, Bond University, Robina, QLD 4226, Australia; [rhea.machado@student.bond.edu.au](rhea.machado@student.bond.edu.au) (R.M.); [reshen.soorinaraindodhy@student.bond.edu.au](reshen.soorinaraindodhy@student.bond.edu.au) (R.S.D.); [atharve.sehgal@student.bond.edu.au](atharve.sehgal@student.bond.edu.au) (A.S.);  \n[kate.rattigan@student.bond.edu.au](kate.rattigan@student.bond.edu.au) (K.R.); [aparna.lalwani@student.bond.edu.au](aparna.lalwani@student.bond.edu.au) (A.L.)  \n* Correspondence: [dwaynfor@bond.edu.au](dwaynfor@bond.edu.au)  \nAbstract: Long-term sequelae of coronavirus disease 2019 (COVID-19) infection are common and can have debilitating consequences. There is a need to understand risk factors for Long COVID-19 to give impetus to the development of targeted yet holistic clinical and public health interventions to reduce its associated healthcare and economic burden. Given the large number and variety of predictors implicated spanning health-related and sociodemographic factors, machine learning becomes a valuable tool. As such, this study aims to employ machine learning to produce an algorithm to predict Long COVID-19 risk, and thereby identify key predisposing factors. Longitudinal cohort data were sourced from the UK’s “Understanding Society: COVID-19 Study”(n = 601 participants with past symptomatic COVID-19 infection confirmed by serology testing) . The random forest classification algorithm demonstrated good overall performance with 97.4% sensitivity and modest specificity (65.4%) . Significant risk factors included early timing of acute COVID-19 infection in the pandemic, greater number of hours worked per week, older age and financial insecurity. Loneliness and having uncommon health conditions were associated with lower risk. Sensitivity analysis suggested that COVID-19 vaccination is also associated","cbCaijkZ5qlNZbI2","https://ap.wps.com/l/cbCaijkZ5qlNZbI2","pdf",910855,1,17,"English","en",105,"# Abstract\n# Introduction\n## Definitions and criteria for Long COVID-19\n# Methods\n## Data source and cohort description\n## Machine learning approach (random forest)\n# Results\n## Model performance metrics\n## Identified risk factors and protective associations\n# Discussion\n## Clinical utility and implications of machine learning\n## Benefits and limitations","[{\"question\":\"What is the main objective of this study on Long COVID-19?\",\"answer\":\"To use machine learning to develop an algorithm that predicts Long COVID-19 risk and helps identify key predisposing factors.\"},{\"question\":\"Which machine learning method was used and how did it perform?\",\"answer\":\"A random forest classification algorithm was used, showing 97.4% sensitivity and 65.4% modest specificity.\"},{\"question\":\"What factors were associated with a higher or lower risk of Long COVID-19?\",\"answer\":\"Higher risk factors included earlier timing of acute infection during the pandemic, more hours worked per week, older age, and financial insecurity; loneliness and uncommon health conditions were associated with lower risk.\"}]","A Machine Learning Approach to Identifying Risk Factors for Long COVID-19 | PDF",1785723297,43,{"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},"a-machine-learning-approach-to-identifying-risk-factors-for-long-covid-19","",{"@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/a-machine-learning-approach-to-identifying-risk-factors-for-long-covid-19/119250/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of this study on Long COVID-19?","Question",{"text":75,"@type":76},"To use machine learning to develop an algorithm that predicts Long COVID-19 risk and helps identify key predisposing factors.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning method was used and how did it perform?",{"text":80,"@type":76},"A random forest classification algorithm was used, showing 97.4% sensitivity and 65.4% modest specificity.",{"name":82,"@type":73,"acceptedAnswer":83},"What factors were associated with a higher or lower risk of Long COVID-19?",{"text":84,"@type":76},"Higher risk factors included earlier timing of acute infection during the pandemic, more hours worked per week, older age, and financial insecurity; 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