[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117437-en":3,"doc-seo-117437-105":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},117437,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","Diagnosis of Pleural Mesothelioma using Machine Learning","Mesothelioma is a cancer that develops in the pleura, most commonly linked to asbestos exposure. Early diagnosis significantly improves survival. This study applies multiple machine learning methods to strengthen pleural mesothelioma detection by extracting features from a preexisting dataset to reduce misclassification. SVM, Decision Trees, and Random Forests are trained using essential, foundational features, evaluated via accuracy, precision, recall, and F1-score in cross-validation. Results indicate strong classifier performance for separating healthy individuals from mesothelioma cases and support the potential for earlier, faster diagnostic decision-making.","Diagnosis of Pleural Mesothelioma using Machine Learning  \nby  \nOlaoluwa Julianah Abejide  \nA thesis submitted in partial fulfillment.  \nof the requirements for the degree of  \nMSc Computational Sciences  \nThe Office of Graduate Studies  \nLaurentian University  \nSudbury, Ontario, Canada  \n© Olaoluwa Julianah Abejide, 2023  \nTHESIS DEFENCE COMMITTEE/COMITÉ DE SOUTENANCE DE THÈSE Laurentian Université/Université Laurentienne  \nOffice of Graduate Studies/Bureau des études supérieures  \nTitle of Thesis  \nTitre de la thèse  \nName of Candidate Nom du candidat  \nDegree Diplôme  \nDepartment/Program  \nDépartement/Programme  \nDiagnosis of Pleural Mesothelioma using Machine Learning  \nAbejide, Olaoluwa Julianah  \nMaster of Science  \nDate of Defence  \nComputational Sciences Date de la soutenance October 26, 2023  \nAPPROVED/APPROUVÉ  \nThesis Examiners/Examinateurs de thèse:  \nDr. Kalpdrum Passi  \n(Supervisor/Directeur(trice) de thèse)  \nDr. Ratvinder Grewak  \n(Committee member/Membre du comité)  \nDr. Oumar Gueye  \n(Committee member/Membre du comité)  \nDr. Jioti Singh Kirar  \n(External Examiner/Examinateur externe)  \nApproved for the Office of Graduate Studies Approuvé pour le Bureau des études supérieures Tammy Eger, PhD  \nVice-President Research (Office of Graduate Studies) Vice-rectrice à la recherche (Bureau des études supérieures) Laurentian University / Université Laurentienne  \nACCESSIBILITY CLAUSE AND PERMISSION TO USE  \nI, Olaoluwa Julianah Abejide, 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 of the 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  \nMesothelioma is cancer that develops in the pleura. The most common cause of this disease is contact with asbestos. Patients with mesothelioma have a better chance of surviving if they are diagnosed quickly. This study utilizes a variety of machine learning to enhance pleural mesothelioma diagnosis. The possibility of misclassification was decreased by extracting features from a preexisting dataset. SVM, Decision Trees, and Random Forests are only a few machine learning classifiers trained using essential and foundational features. Accuracy, precision, recall, and F1-score were just a few measures used to evaluate these classifiers' performance in crossvalidation. SVM demonstrated excellent accuracy, precision, recall, and F1-score when classifying individuals as either healthy or having mesothelioma. The results show the potential of machine learning techniques for early diagnosis of pleural mesothelioma. Machine learning algorithms improve diagnosis accuracy and turnaround time, improving patient outcomes. Using the results of this research, a fully automated technique for diagnosing mesothelioma might be developed, allowing clinicians more time to provide better care for their patients.  \nKeywords: Mesothelioma, Machine Learning, SVM, Anfis, Decision Trees, and Random For","cbCaim8gtAJPSAdw","https://ap.wps.com/l/cbCaim8gtAJPSAdw","pdf",792849,1,82,"English","en",105,"# Abstract\n# Introduction\n# Methodology\n## Feature Extraction\n## Classifier Training\n## Model Evaluation\n# Results\n# Discussion\n# Conclusion\n# Acknowledgements","[{\"question\":\"What causes pleural mesothelioma, and why does early diagnosis matter?\",\"answer\":\"Pleural mesothelioma is cancer of the pleura, and the most common cause is contact with asbestos. Patients have a better chance of surviving when diagnosed quickly.\"},{\"question\":\"Which machine learning classifiers were trained in this study?\",\"answer\":\"The study trains SVM, Decision Trees, and Random Forests using essential, foundational features extracted from a preexisting dataset.\"},{\"question\":\"How were the classifiers evaluated, and what were the key findings?\",\"answer\":\"Performance was assessed using accuracy, precision, recall, and F1-score in cross-validation. SVM showed excellent results when classifying healthy individuals versus those with mesothelioma, supporting the potential for early diagnosis.\"}]","Diagnosis of Pleural Mesothelioma using Machine Learning | PDF",1785675872,207,{"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},"diagnosis-of-pleural-mesothelioma-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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/diagnosis-of-pleural-mesothelioma-using-machine-learning/117437/",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","2026-08-02",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 causes pleural mesothelioma, and why does early diagnosis matter?","Question",{"text":75,"@type":76},"Pleural mesothelioma is cancer of the pleura, and the most common cause is contact with asbestos. Patients have a better chance of surviving when diagnosed quickly.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning classifiers were trained in this study?",{"text":80,"@type":76},"The study trains SVM, Decision Trees, and Random Forests using essential, foundational features extracted from a preexisting dataset.",{"name":82,"@type":73,"acceptedAnswer":83},"How were the classifiers evaluated, and what were the key findings?",{"text":84,"@type":76},"Performance was assessed using accuracy, precision, recall, and F1-score in cross-validation. 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