[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127939-en":3,"doc-seo-127939-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127939,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","Adverse Effects of COVID-19 Vaccination - Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccination Reactogenicity","Good vaccine safety and reliability are essential for countering infectious disease spread. A small but significant number of adverse reactions to COVID-19 vaccines have been reported, motivating patient-data driven efforts to detect common factors. The study analyzes medical histories and postvaccination outcomes using statistical methods and machine-learning classification. Findings link poor reactions with prior illnesses, hospital admission, SARS-CoV-2 reinfection, and pre-existing comorbidities, while clinical predictors include dyspnoea and multiple pains. Models trained on history achieved over 90% accuracy and support targeted monitoring.","Article  \nAdverse Effects of COVID-19 Vaccination: Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccination Reactogenicity  \nMd. Martuza Ahamad 1, Sakifa Aktar 1, Md. Jamal Uddin 1, Md. Rashed-Al-Mahfuz 2, A. K. M. Azad 3, Shahadat Uddin 4, Salem A. Alyami 3, Iqbal H. Sarker 5, Asaduzzaman Khan 6, Pietro Liò 7,  \nJulian M. W. Quinn 8 and Mohammad Ali Moni 6,􀀃  \nCitation: Ahamad, M.M.; Aktar, S.; Uddin, M.J.; Rashed-Al-Mahfuz, M.; Azad, A.K.M.; Uddin, S.; Alyami, S.A.; Sarkar, I.H.; Khan, A.; Liò, P.; et al. Adverse Effects of COVID-19 Vaccination: Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccination Reactogenicity. Healthcare 2023, 11, 31. [https://](https://)[ ](https://)[doi.org/10.3390/healthcare11010031](doi.org/10.3390/healthcare11010031)  \nAcademic Editor: Joaquim Carreras  \nReceived: 12 October 2022  \nRevised: 8 December 2022  \nAccepted: 13 December 2022  \nPublished: 22 December 2022  \nCopyright: © 2022 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 Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh  \n2 Department of Computer Science and Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh  \n3 Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia  \n4 Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Darlington, NSW 2008, Australia  \n5 Department of Computer Science and Engineering, Chittagong University of Engineering & Technology, Chittagong 4349, Bangladesh  \n6 School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD 4072, Australia  \n7 Computer Laboratory, The University of Cambridge, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK  \n8 Healthy Ageing, The Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia  \n* [Correspondence: m.moni@uq.edu.au](Correspondence: m.moni@uq.edu.au)  \nAbstract: Good vaccine safety and reliability are essential for successfully countering infectious disease spread. A small but significant number of adverse reactions to COVID-19 vaccines have been reported. Here, we aim to identify possible common factors in such adverse reactions to enable strategies that reduce the incidence of such reactions by using patient data to classify and characterise those at risk. We examined patient medical histories and data documenting postvaccination effects and outcomes. The data analyses were conducted using a range of statistical approaches followed by a series of machine learning classification algorithms. In most cases, a group of similar features was significantly associated with poor patient reactions. These included patient prior illnesses, admission to hospitals and SARS-CoV-2 reinfection. The analyses indicated that patient age, gender, taking other medications, type-2 diabetes, hypertension, allergic history and heart disease are the most significant pre-existing factors associated with the risk of poor outcome. In addition, long duration of hospital treatments, dyspnoea, various kinds of pain, headache, cough, asthenia, and physical disability were the most significant clinical predictors. The machine learning classifiers that are trained with medical history were also able to predict patients with complication-free vaccination and have an accuracy score above 90% . Our study identifies profiles of individuals that may need extra monitoring and care (e.g., vaccination at a location with access t","cbCaib8SuW7Jv9nl","https://ap.wps.com/l/cbCaib8SuW7Jv9nl","pdf",4912106,3,1,22,"English","en",105,"# Introduction\n## Study Aim and Data Sources\n## Statistical and Machine Learning Methods\n## Key Risk Factors and Clinical Predictors\n## Model Performance and Implications","[{\"question\":\"What is the primary goal of the study on COVID-19 vaccination adverse effects?\",\"answer\":\"To identify common factors associated with adverse reactions by classifying and characterizing individuals at risk using patient data.\"},{\"question\":\"Which patient factors were reported as significant pre-existing risks for poor outcomes?\",\"answer\":\"Age, gender, use of other medications, type-2 diabetes, hypertension, allergic history, and heart disease were highlighted as significant factors.\"},{\"question\":\"What symptoms or clinical features were identified as important predictors?\",\"answer\":\"Long duration of hospital treatments, dyspnoea, various kinds of pain, headache, cough, asthenia, and physical disability were reported as significant clinical predictors.\"}]","Adverse Effects of COVID-19 Vaccination - Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccination Reactogenicity | PDF",1785943108,55,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"adverse-effects-of-covid-19-vaccination-machine-learning-and-statistical-approach-to-identify-and-classify-incidences-of-morbidity-and-postvaccination-reactogenicity","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/healthcare/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/adverse-effects-of-covid-19-vaccination-machine-learning-and-statistical-approach-to-identify-and-classify-incidences-of-morbidity-and-postvaccination-reactogenicity/127939/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the primary goal of the study on COVID-19 vaccination adverse effects?","Question",{"text":76,"@type":77},"To identify common factors associated with adverse reactions by classifying and characterizing individuals at risk using patient data.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which patient factors were reported as significant pre-existing risks for poor outcomes?",{"text":81,"@type":77},"Age, gender, use of other medications, type-2 diabetes, hypertension, allergic history, and heart disease were highlighted as significant factors.",{"name":83,"@type":74,"acceptedAnswer":84},"What symptoms or clinical features were identified as important predictors?",{"text":85,"@type":77},"Long duration of hospital treatments, dyspnoea, various kinds of pain, headache, cough, asthenia, and physical disability were reported as significant clinical predictors.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,119,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":47,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]