[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120110-en":3,"doc-seo-120110-105":30,"detail-sidebar-cat-0-en-105":95},{"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":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},120110,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Breast Cancer Diagnostic Using Machine Learning - Applying Supervised Learning Techniques to Coimbra and Wisconsin Datasets - Master’s Thesis 2023","Breast cancer represents a major global health challenge, reported with about 2.2 million new cases and 700,000 deaths in 2020. Conventional diagnosis relies heavily on expert judgement and can show notable variation in accuracy. Supervised machine learning models are applied to improve early detection and diagnostic precision, evaluating Decision Trees, K-Nearest Neighbors, Support Vector Machines, and Logistic Regression on the Coimbra and Wisconsin datasets. Model comparison includes hyperparameter and distance-measure optimization, with feature selection and Principal Component Analysis to identify effective configurations.","BREAST CANCER DIAGNOSTIC USING MACHINE LEARNING  \nApplying Supervised Learning Techniques to Coimbra and Wisconsin Datasets  \nLappeenranta–Lahti University of Technology LUTMaster’s Programme in Computational Engineering 2023  \nVikas Kushwaha  \nExaminers: Associate Professor Lassi Roininen  \nPost-Doctoral Researcher Jyrki Savolainen  \nABSTRACT  \nLappeenranta–Lahti University of Technology LUTLUT School of Engineering Science Computational Engineering  \nVikas Sudarshan Kushwaha  \nBREAST CANCER DIAGNOSTIC USING MACHINE LEARNING  \nApplying Supervised Learning Techniques to Coimbra and Wisconsin Datasets  \nMaster’s thesis 2023  \n71 pages, 13 figures, 12 tables  \nExaminers:Associate Professor Lassi Roininen and Jyrki Savolainen, Post-Doctoral Researcher  \nKeywords: Breast cancer diagnosis, Machine learning, Cancer detection, Predictive Modelling  \nBreast cancer poses a significant global health concern, with approximately 2.2 million new cases and 700,000 deaths reported in 2020. Traditional diagnostic approaches which predominantly depend on expert judgement, have been associated with substantial variability in accuracy. To bridge this gap ML models are used to improve diagnostic out of which the present research investigates the potential of specific machine learning algorithms—Decision Trees, K-Nearest Neighbors, Support Vector Machines, and Logistic Regression—with an overarching objective of improving early detection and enhancing the precision of breast cancer diagnosis. The study utilizes the Breast Cancer Coimbra Dataset and the Wisconsin Diagnostic Breast Cancer Dataset for model training and evaluation. A comprehensive comparative analysis of these models is conducted, with a focus on optimizing hyperparameters and distance measures to ascertain the most effective configurations. Further, the influence of feature selection methods and Principal Component Analysis on model performance is explored.  \nLogistic Regression and Support Vector Machines models demonstrated remarkable performance, surpassing the predictive accuracy of models reported in current literature, with accuracies reaching up to 99.42% . This research could serve as a foundation for future studies applying machine learning models in breast cancer diagnostics, emphasizing the potential of machine learning as a robust tool in medical diagnostics.  \nACKNOWLEDGEMENTS  \nI want to express my deepest gratitude to my mom, dad, and brothers who have provided unwavering support throughout my master's journey. Their love and reassurance during times of doubt have provided the strength I needed to persevere. I can confidently say that this achievement would not have been possible without them. I am immensely thankful for their enduring faith in my abilities and for their ceaseless support.  \nI would also like to thank my supervisors, Jyrki Savolainen and Lassi Roininen, for their invaluable feedback, which has greatly enhanced the quality and clarity of this work.  \nABBREVIATIONS  \nBC Breast Cancer  \nML Machine Learning  \nBCCD Breast Cancer Coimbra Dataset  \nWDBC Wisconsin Diagnostic Breast Cancer  \nKNN K-Nearest Neighbors  \nSVM Support Vector Machines  \nLR Logistic Regression  \nDT Decision Tree  \nDCIS Ductal Carcinoma in Situ  \nASIR Age-Standardized Incidence Rate  \nHDI Human Development Index  \nDALY Disability-Adjusted Life Years  \nHER2 Human Epidermal growth factor Receptor 2 BRCA1 Breast Cancer gene 1  \nBRCA2 Breast Cancer gene 2  \nHRT Hormone Replacement Therapy  \nMIR Mortality-to-Incidence Ratio  \nPCA Principal Component Analysis  \nTP True Positive  \nTN True Negative  \nFP False Positive  \nFN False Negative  \nTable of contents  \nAbstract  \nAcknowledgments  \nSymbols and abbreviations  \n1. Introduction ................................................................................................................... 8  \n1.1. Background and Motivation................................................................................... 9  \n1.2. Aim and research questio","cbCaicstVGdsGoSd","https://ap.wps.com/l/cbCaicstVGdsGoSd","pdf",1783630,1,72,"English","en",105,"# Abstract\n# Acknowledgments\n# Symbols and abbreviations\n# 1. Introduction\n## 1.1. Background and Motivation\n## 1.2. Aim and research question\n## 1.2.1. Significance of study\n# 2. Theoretical Background\n## 2.1. Breast cancer\n## 2.2. Machine learning\n## 2.3. Algorithms\n## 2.4. Assessing Machine Learning Models: The Key Role of Precision, Recall, F1-score, and Accuracy","[{\"question\":\"Which supervised learning algorithms are investigated for breast cancer diagnosis?\",\"answer\":\"The study evaluates Decision Trees, K-Nearest Neighbors, Support Vector Machines, and Logistic Regression.\"},{\"question\":\"What datasets are used for training and evaluation?\",\"answer\":\"Training and evaluation use the Breast Cancer Coimbra Dataset and the Wisconsin Diagnostic Breast Cancer Dataset.\"},{\"question\":\"How is model performance improved and compared in the research?\",\"answer\":\"Performance is compared through hyperparameter and distance-measure optimization, and by analyzing the impact of feature selection methods and Principal Component Analysis (PCA).\"},{\"question\":\"What results do Logistic Regression and Support Vector Machines achieve?\",\"answer\":\"Logistic Regression and Support Vector Machines show strong predictive performance, reaching accuracies up to 99.42% and surpassing reported accuracies in current literature.\"}]","Breast Cancer Diagnostic Using Machine Learning - Applying Supervised Learning Techniques to Coimbra and Wisconsin Datasets - Master’s Thesis 2023 | PDF",1785728246,181,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"breast-cancer-diagnostic-using-machine-learning-applying-supervised-learning-techniques-to-coimbra-and-wisconsin-datasets-masters-thesis-2023","",{"@graph":36,"@context":89},[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/breast-cancer-diagnostic-using-machine-learning-applying-supervised-learning-techniques-to-coimbra-and-wisconsin-datasets-masters-thesis-2023/120110/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"Which supervised learning algorithms are investigated for breast cancer diagnosis?","Question",{"text":75,"@type":76},"The study evaluates Decision Trees, K-Nearest Neighbors, Support Vector Machines, and Logistic Regression.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What datasets are used for training and evaluation?",{"text":80,"@type":76},"Training and evaluation use the Breast Cancer Coimbra Dataset and the Wisconsin Diagnostic Breast Cancer Dataset.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance improved and compared in the research?",{"text":84,"@type":76},"Performance is compared through hyperparameter and distance-measure optimization, and by analyzing the impact of feature selection methods and Principal Component Analysis (PCA).",{"name":86,"@type":73,"acceptedAnswer":87},"What results do Logistic Regression and Support Vector Machines achieve?",{"text":88,"@type":76},"Logistic Regression and Support Vector Machines show strong predictive performance, reaching accuracies up to 99.42% and surpassing reported accuracies in current literature.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]