[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120709-en":3,"doc-seo-120709-105":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},120709,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","A Different Traditional Approach for Automatic Comparative Machine Learning in Multimodality Covid-19 Severity Recognition","In March 2020, the WHO introduced COVID-19, a novel coronavirus pandemic that spread from Wuhan and generated major global health burdens. Unimodal AI diagnosis contributed to a substantial rate of false negatives, motivating multimodal severity modeling. This paper integrates 2500 COVID-19 multimodal data using the Early Fusion Type-I (EFT1) architecture for severity classification. One-step AutoCML and AutoIFSCML frameworks are built with the Descended Composite Scores Average (DCSA) assessment. Results show Extreme Gradient Boost (DCSA=0.998) in AutoCML and Random Forest (DCSA=0.960) in AutoIFSCML, with 70% high-DCSA features selected by AutoIFS.","International journal of innovation in Engineering, Vol 3, No 1, (2023), 1-12  \n\n|  | International journal of innovation in Engineering\u003Cbr>journal [homepage: www.ijie.ir](homepage: www.ijie.ir) |  |  |\n| --- | --- | --- | --- |\n| Research Paper\u003Cbr>A Different Traditional Approach for Automatic Comparative Machine Learning in Multimodality Covid-19 Severity Recognition\u003Cbr>Mohammadreza Saraeia1, Saba Rahmanib, Saman Rajebia, Sebelan Danishvara\u003Cbr>a Biomedical Engineering Faculty, Seraj Higher Education Institute, Tabriz, Iran\u003Cbr>b Assistant Professor, Electrical Engineering Faculty, Seraj Higher Education Institute, Tabriz, Iran c Research Fellow, College of Engineering, Design, and Physical Sciences, Brunel University, London, UK |  |  |  |\n| A R T I C L E I N F O A B S T R A C T |  |  |  |\n| Received: 01 January 2023\u003Cbr>Reviewed: 06 January 2023\u003Cbr>Revised: 22 February 2023\u003Cbr>Accepted: 24 February 2023\u003Cbr>Keywords:\u003Cbr>Machine Learning, Covid-19, Multimodality, Severity recognition, Computer-Assisted, Classification. |  | In March 2020, the world health organization introduced a new infectious pandemic called “novel coronavirus disease” or “Covid-19”, origin dates back to World War II (1939) and spread from the city of Wuhan in China (2019) . The severity of the outbreak affected the health of abundant folk worldwide. This bred the emergence of unimodal artificial intelligence approaches in the diagnosis of coronavirus disease but solely led to a significant percentage of false-negative results. In this paper, we combined 2500 Covid-19 multimodal data based on Early Fusion Type-I (EFT1) architecture as a severity recognition model for the classification task. We designed and implemented one-step systems of automatic comparative machine learning (AutoCML) and automatic comparative machine learning based on important feature selection (AutoIFSCML) . We utilized our posed assessment method called “Descended Composite Scores Average (DCSA)”. In AutoCML, Extreme Gradient Boost (DCSA=0.998) and in AutoIFSCML, Random Forest (DCSA=0.960) demonstrated the best performance for multimodality Covid-19 severity recognition while 70% of the characteristics with high DCSA were chosen by the internal important features selection system (AutoIFS) to enter the AutoCML system. The DCSA-based designed systems can be useful in implementing fine-tuned machine learning models in medical processes by leveraging the capacities and performances of the model in all methods. As well as, ensemble learning made sounds good among evaluated traditional models in systems. |  |\n\n1 Corresponding Author  \n[mrsaraei3@gmail.com](mrsaraei3@gmail.com)  \n1. Introduction  \nThe origins ofthe coronavirus go back to World War II (Zaim, Chong, Sankaranarayanan, & Harky, 2020), which has been identified by the World Health Organization (WHO) as a “Pandemic” since 2020 (Rubin, et al., 2020) . The most common clinical signs of Covid-19 are fever and cough or at least three signs of pulmonary and gastrointestinal problems. The severity of the disease is divided into five categories based on grading: asymptomatic, mild, moderate, severe, and critical (AMMSC) (Wang, Kang, Liu, & Tong, 2020) and ways to diagnose Covid-19 are the use of laboratory results of real-time polymerase chain reaction (PCR) and computed tomography (CT) of the lung (MOHME, 2021) but under different conditions, various other tests are taken, such as complete blood count (CBC), intracellular enzyme lactate dehydrogenase (LDH), erythrocyte sedimentation rate (ESR), blood clotting (D-dimer), and C-reactive protein (CRP), but each of these methods alone cannot correctly diagnose and adjust timely treatment plan for corona’s patients (Li, et al., 2020). For example, the results ofan RT-PCR at the beginning of the disease period demonstrate a 40% false-negative result (Li, et al., 2020). Therefore, methods based on machine learning (ML) (until 2010), deep learning (DL) (from 2010 onwards) and fuzzy inference sy","cbCait5LdAhkR4tZ","https://ap.wps.com/l/cbCait5LdAhkR4tZ","pdf",1493232,1,12,"English","en",105,"# Introduction\n## Clinical signs and severity categories\n## Diagnosis methods and limitations\n## Multimodal data and AI approaches","[{\"question\":\"What problem does the paper address in COVID-19 severity prediction?\",\"answer\":\"The work addresses the high false-negative rate caused by unimodal AI diagnosis, which can hinder timely and accurate severity assessment for COVID-19 patients.\"},{\"question\":\"How does the proposed method incorporate multimodal information?\",\"answer\":\"It combines 2500 COVID-19 multimodal data using the Early Fusion Type-I (EFT1) architecture to support severity classification.\"},{\"question\":\"What models and evaluation approach are used in the comparative machine learning systems?\",\"answer\":\"The paper uses the Descended Composite Scores Average (DCSA) assessment, with Extreme Gradient Boost performing best in AutoCML (DCSA=0.998) and Random Forest best in AutoIFSCML (DCSA=0.960).\"}]","A Different Traditional Approach for Automatic Comparative Machine Learning in Multimodality Covid-19 Severity Recognition | 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problem does the paper address in COVID-19 severity prediction?","Question",{"text":75,"@type":76},"The work addresses the high false-negative rate caused by unimodal AI diagnosis, which can hinder timely and accurate severity assessment for COVID-19 patients.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method incorporate multimodal information?",{"text":80,"@type":76},"It combines 2500 COVID-19 multimodal data using the Early Fusion Type-I (EFT1) architecture to support severity classification.",{"name":82,"@type":73,"acceptedAnswer":83},"What models and evaluation approach are used in the comparative machine learning systems?",{"text":84,"@type":76},"The paper uses the Descended Composite Scores Average (DCSA) assessment, with Extreme Gradient Boost performing best in AutoCML (DCSA=0.998) and Random Forest best in AutoIFSCML 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