[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124231-en":3,"doc-seo-124231-105":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},124231,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Early Risk Prediction in Acute Aortic Syndrome - On Clinical Data Using Machine Learning","Advancements in machine learning enable early prediction of Acute Aortic Syndrome (AAS), a critical and life-threatening clinical condition, by identifying influential features. The study integrates, cleans, and processes large clinical datasets from 150 emergency departments across Canada and the USA, covering nearly 150,000 patients from 2021 to 2022 with categorical variables. Predictive models are built using multiple data-splitting strategies and classifiers. Feature handling includes missing data integration via multiple imputation and class-imbalance mitigation via SMOTE. Feature extraction and dimensionality reduction use PCA and selection methods such as CFS and Relief, achieving the highest accuracy with Relief features and Random Forest (99.3%) and supporting future cardiovascular research.","Early Risk Prediction in Acute Aortic Syndrome On Clinical Data Using Machine Learning  \nby  \nMehdi Tavafi  \nA thesis submitted in partial fulfillment of the requirements for the degree of  \nMaster of Science (MSc) in Computational Science  \nThe Faculty of Graduate Studies  \nLaurentian University  \nSudbury, Ontario, Canada  \n© Mehdi Tavafi, 2024  \nTHESIS DEFENCE COMMITTEE/COMITÉ DE SOUTENANCE DE THÈSE Laurentian Université/Université Laurentienne  \nFaculty of Graduate Studies/Faculté des études supérieures  \nTitle of Thesis  \nTitre de la thèse  \nName of Candidate Nom du candidat  \nDegree Diplôme  \nDepartment/Program  \nDépartement/Programme  \nEarly Risk Prediction in Acute Aortic Syndrome On Clinical Data Using Machine Learning  \nTafavi, Mehdi  \nMaster of Science  \nDate of Defence  \nComputational Sciences Date de la soutenance April, 2024  \nAPPROVED/APPROUVÉ  \nThesis Examiners/Examinateurs de thèse:  \nDr. Kalpdrum Passi  \n(Co-Supervisor/Co-directeur(trice) de thèse)  \nDr. Robert Ohle  \n(Co-Supervisor/Co-directeur(trice) de thèse)  \nDr. Ratvinder Grewal  \n(Committee member/Membre du comité)  \nDr. Vasu Appanna  \n(Committee member/Membre du comité)  \nDr. R. Kanchana  \n(External Examiner/Examinateur externe)  \nApproved for the Faculty of Graduate Studies Approuvé pour la Faculté des études supérieures Dr. Tammy Eger  \nMadame Tammy Eger  \nVP Research (Graduate Studies)  \nVR, Recherche (Études supérieures)  \nACCESSIBILITY CLAUSE AND PERMISSION TO USE  \nI, Mehdi Tafavi, hereby grant to Laurentian University and/or its agents the non-exclusive license to archive and make accessible my thesis, dissertation, or project report in whole or in part in all forms of media, now or for the duration of my copyright ownership. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also reserve the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. I further agree that permission for copying of this thesis in any manner, in whole or in part, for scholarly purposes may be granted by the professor or professors who supervised my thesis work or, in their absence, by the Head of the Department in which my thesis work was done. It is understood that any copying or publication or use of this thesis or parts thereof for financial gain shall not be allowed without my written permission. It is also understood that this copy is being made available in this form by the authority ofthe copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.  \nAbstract  \nAdvancements in machine learning present novel opportunities for early prediction of Acute Aortic Syndrome (AAS) as a critical and life-threatening clinical condition and the identification of critical features influencing this prediction. This study concentrates on integrating, cleaning, and handling missing data from extensive clinical datasets sourced from 150 emergency departments across Canada and the USA. Covering medical histories of nearly 150,000 patients from 2021 to 2022, the dataset comprises categorical clinical variables. Additionally, the research focuses on constructing predictive machine learning models utilizing various data-splitting strategies and classifiers to optimize AAS prediction. Methodologically, the study encompasses data identification, acquisition, exploration, processing, and feature extraction, followed by dimensionality reduction using Principal Component Analysis (PCA) and other feature selection methods such as Correlation-based (CFS) and Relief. The multiple imputations method and the SMOTE method are utilized for handling missing and imbalanced data, respectively. The findings demonstrate that employing the Relief-feature method with an 80-10-10 split ratio alongside the Random Forest classifier yields an exceptional acc","cbCaijK29L8bX891","https://ap.wps.com/l/cbCaijK29L8bX891","pdf",2735918,1,154,"English","en",105,"# Abstract\n# Keywords\n# Acknowledgments","[{\"question\":\"What clinical problem does this thesis address?\",\"answer\":\"The thesis focuses on early prediction of Acute Aortic Syndrome (AAS), a critical and life-threatening condition.\"},{\"question\":\"How is the dataset prepared before model training?\",\"answer\":\"The study integrates and cleans clinical data from 150 emergency departments, handles missing values using multiple imputation, and addresses imbalance using SMOTE.\"},{\"question\":\"Which modeling approach and feature strategy achieved the best performance?\",\"answer\":\"Relief-based feature selection with an 80-10-10 split combined with the Random Forest classifier produced the highest accuracy of 99.3%.\"}]","Early Risk Prediction in Acute Aortic Syndrome - On Clinical Data Using Machine Learning | PDF",1785821140,388,{"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},"early-risk-prediction-in-acute-aortic-syndrome-on-clinical-data-using-machine-learning","",{"@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/early-risk-prediction-in-acute-aortic-syndrome-on-clinical-data-using-machine-learning/124231/",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},"What clinical problem does this thesis address?","Question",{"text":75,"@type":76},"The thesis focuses on early prediction of Acute Aortic Syndrome (AAS), a critical and life-threatening condition.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the dataset prepared before model training?",{"text":80,"@type":76},"The study integrates and cleans clinical data from 150 emergency departments, handles missing values using multiple imputation, and addresses imbalance using SMOTE.",{"name":82,"@type":73,"acceptedAnswer":83},"Which modeling approach and feature strategy achieved the best performance?",{"text":84,"@type":76},"Relief-based feature selection with an 80-10-10 split combined with the Random Forest classifier produced the highest accuracy of 99.3%.","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"]