[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123053-en":3,"doc-seo-123053-105":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},123053,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","COVID-19 Severity Prediction Using Combined Machine Learning and Transfer Learning Approaches - Research highlights","The global spread of Coronavirus Disease 2019 (COVID-19) created an urgent need for accurate tools that help healthcare providers anticipate how infection progresses. This study applies machine learning and transfer learning to forecast coronavirus severity in two stages. Blood parameters support early screening using a probabilistic stacked ensemble with modified mutual information feature selection, while CT-scan images drive staging via a transfer learning mResNet-50 model. Results show 97.79% accuracy for infection stages and strong generalizability across benchmark datasets.","Article – Engineering, Technology and Techniques  \nCOVID-19 Severity Prediction Using Combined Machine Learning and Transfer Learning Approaches  \nAme Rayan Rambola1*  \n[https://orcid.org/0000-0001-9536-8622](https://orcid.org/0000-0001-9536-8622)  \nSuruliandi Andavar2  \n[https://orcid.org/0000-0002-2863-1555](https://orcid.org/0000-0002-2863-1555)  \nRaja Soosaimarian Peter Raj3  \n[https://orcid.org/0000-0002-7216-2207](https://orcid.org/0000-0002-7216-2207)  \n1Manonmaniam Sundaranar University, Department of Computer Science and Engineering, Tirunelveli, Tamil Nadu, India; 2Manonmaniam Sundaranar University, Department of Computer Science and Engineering, Tirunelveli, Tamil Nadu, India; 3Vellore Institute of Technology, School of Computer Science and Engineering, Vellore, Tamil Nadu, India.  \nEditor-in-Chief: Alexandre Rasi Aoki  \nAssociate Editor: Alexandre Rasi Aoki  \nReceived: 30-Mar-2024; Accepted: 25-Jun-2024  \n*Correspondence: [amerayanld@gmail.com](amerayanld@gmail.com) ; Tel.: +91-7598460218 (A. R. R. ) .  \nHIGHLIGHTS  \n• Proposed a hybrid feature selection method.  \n• Proposed a stacked ensemble classifier.  \n• Proposed a transfer learning approach for image classification.  \n• Developed a model with the proposed techniques and measured its efficacy.  \nAbstract: The global spread of Coronavirus Disease 2019 (COVID-19) has resulted in an extensive pandemic, with the virus rapidly transmitting through interactions among infected individuals, presenting a substantial threat to healthcare professionals. In response, computer scientists have employed artificial intelligence methodologies to identify and address COVID-19. This study utilizes machine learning and transfer learning techniques to forecast the severity of the coronavirus, aiding healthcare providers in determining the progression of the illness in patients. Prediction of disease severity occurs in two stages. Initially, blood parameter values are utilized for preliminary screening of coronavirus infection through machine learning methods. The first stage employs the proposed Probabilistic Stacked Ensemble Classifier, employing optimal features selected using the proposed Modified Mutual Information feature selection algorithm, to detect the presence or absence of the virus. Following that, the subsequent phase employs proposed mResNet-50, a transfer learning approach, which utilizes Computed Tomography (CT)-scan images to predict the stage of infection in affected individuals. Experimental results indicate that the model achieves a 97.79% accuracy rate in forecasting infection stages and demonstrates the generalizability of the proposed model across benchmark datasets.  \nKeywords: Machine Learning; Transfer Learning; COVID-19; clinical data; CT-Scan.  \nINTRODUCTION  \nIn late December 2019, an infectious virus named Corona was initially identified in an individual in Wuhan, China, sparking a widespread health crisis. The virus rapidly spread, causing a considerable number of individuals who had contact with the infected person to contract the illness. Various screening methods, including laboratory tests, Reverse Transcription-Polymerase Chain Reaction (RT-PCR) , X-rays, and CT scans, are utilized to detect the disease. RT-PCR is commonly employed as the primary method for early detection, despite occasional inaccuracies and its time-consuming and costly nature [1] . Consequently, healthcare professionals have proposed an alternative approach: analysing blood samples from affected individuals. Upon entry into the body, the virus triggers specific changes that can be exploited to determine if someone is infected with the coronavirus. Blood tests provide rapid and cost-effective results, making them an appealing initial screening method. CT scans are recommended for coronavirus-affected patients since X-ray images, despite their advantages, are less sensitive and may lead to false predictions in cases of early or mild infection.  \nMachine learning algorithms play","cbCaifpForHs38VM","https://ap.wps.com/l/cbCaifpForHs38VM","pdf",1008669,1,18,"English","en",105,"# Highlights\n## Proposed methods\n# Introduction\n## Pandemic background and detection challenges\n## Two-phase prediction objective\n# Method Contributions (overview)","[{\"question\":\"How does the model predict COVID-19 severity in two stages?\",\"answer\":\"The first stage screens infection presence/absence using blood parameters with a probabilistic stacked ensemble and modified mutual information feature selection. The second stage predicts infection stage from CT-scan lung images using a transfer learning approach (mResNet-50).\"},{\"question\":\"What role does the modified mutual information (MMI) feature selection play?\",\"answer\":\"MMI selects optimal relevant features from the clinical dataset before the classifier performs early detection, improving the signal used for identifying infection presence/absence.\"},{\"question\":\"What performance and generalization results were reported?\",\"answer\":\"Experimental results indicate 97.79% accuracy for forecasting infection stages and demonstrate generalizability of the proposed model across benchmark datasets.\"}]","COVID-19 Severity Prediction Using Combined Machine Learning and Transfer Learning Approaches - Research highlights | PDF",1785814413,45,{"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-severity-prediction-using-combined-machine-learning-and-transfer-learning-approaches-research-highlights","",{"@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-severity-prediction-using-combined-machine-learning-and-transfer-learning-approaches-research-highlights/123053/",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},"How does the model predict COVID-19 severity in two stages?","Question",{"text":75,"@type":76},"The first stage screens infection presence/absence using blood parameters with a probabilistic stacked ensemble and modified mutual information feature selection. The second stage predicts infection stage from CT-scan lung images using a transfer learning approach (mResNet-50).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role does the modified mutual information (MMI) feature selection play?",{"text":80,"@type":76},"MMI selects optimal relevant features from the clinical dataset before the classifier performs early detection, improving the signal used for identifying infection presence/absence.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance and generalization results were reported?",{"text":84,"@type":76},"Experimental results indicate 97.79% accuracy for forecasting infection stages and demonstrate generalizability of the proposed model across benchmark datasets.","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"]