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dc.contributor.authorAk, Muhammet Fatih
dc.date.accessioned2021-09-28T12:48:11Z
dc.date.available2021-09-28T12:48:11Z
dc.date.issued2020
dc.identifier.citationAk, M. F. (2020). A comparative analysis of breast cancer detection and diagnosis using data visualization and machine learning applications. Healthcare, 8(2). 111-134.en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12566/881
dc.description.abstractIn the developing world, cancer death is one of the major problems for humankind. Even though there are many ways to prevent it before happening, some cancer types still do not have any treatment. One of the most common cancer types is breast cancer, and early diagnosis is the most important thing in its treatment. Accurate diagnosis is one of the most important processes in breast cancer treatment. In the literature, there are many studies about predicting the type of breast tumors. In this research paper, data about breast cancer tumors from Dr. William H. Walberg of the University of Wisconsin Hospital were used for making predictions on breast tumor types. Data visualization and machine learning techniques including logistic regression, k-nearest neighbors, support vector machine, naïve Bayes, decision tree, random forest, and rotation forest were applied to this dataset. R, Minitab, and Python were chosen to be applied to these machine learning techniques and visualization. The paper aimed to make a comparative analysis using data visualization and machine learning applications for breast cancer detection and diagnosis. Diagnostic performances of applications were comparable for detecting breast cancers. Data visualization and machine learning techniques can provide significant benefits and impact cancer detection in the decision-making process. In this paper, different machine learning and data mining techniques for the detection of breast cancer were proposed. Results obtained with the logistic regression model with all features included showed the highest classification accuracy (98.1%), and the proposed approach revealed the enhancement in accuracy performances. These results indicated the potential to open new opportunities in the detection of breast cancer.en_US
dc.description.sponsorshipNo sponsoren_US
dc.language.isoengen_US
dc.publisherHealthcareen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBreast canceren_US
dc.subjectData visualizationen_US
dc.subjectEarly diagnosisen_US
dc.subjectMachine learningen_US
dc.subjectRisk assessmenten_US
dc.subjectMeme kanseritr_TR
dc.subjectVeri görüntülemetr_TR
dc.subjectErken tanıtr_TR
dc.subjectMakine öğrenmetr_TR
dc.subjectRisk değerlendirmesitr_TR
dc.titleA comparative analysis of breast cancer detection and diagnosis using data visualization and machine learning applicationsen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.relation.publicationcategoryInternational publicationen_US
dc.identifier.wosWOS:000548056900049
dc.identifier.scopus2-s2.0-85097136935
dc.identifier.volume8
dc.identifier.issue2
dc.identifier.startpage111
dc.identifier.endpage134
dc.contributor.orcid0000-0003-4342-296X [Ak, Muhammet Fatih]
dc.contributor.abuauthorAk, Muhammet Fatih
dc.contributor.yokid279243 [Ak, Muhammet Fatih]
dc.contributor.ScopusAuthorID57191904300 [Ak, Muhammet Fatih]
dc.identifier.PubMedID32357391
dc.identifier.none134
dc.identifier.doidoi:10.3390/healthcare8020111


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