The Investigation of the Success of Different Machine Learning Methods in Breast Cancer Diagnosis

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dc.authorid0000-0002-9245-5728en_US
dc.contributor.authorAtes, Ibrahim
dc.contributor.authorBilgin, Turgay Tugay
dc.date.accessioned2022-08-05T06:28:45Z
dc.date.available2022-08-05T06:28:45Z
dc.date.issued2021en_US
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractObjective: The aim of this study is to identify cancer earlier in life using machine learning methods. Methods: For this purpose, the Wisconsin Diagnostic Breast Cancer dataset was classified using Naive Bayes, decision trees, artificial neural networks algorithms and comparison of these machine learning methods was made. KNIME Analytics Platform was used for applications. Before the classification process, the dataset was preprocessed. After the preprocessing stage, three different classifier methods were applied to the dataset. Accuracy, sensitivity, specificity and confusion matrices were used to measure the success of the methods. Results: The results show that Naive Bayes and artificial neural network methods classify tumors with 96.5% accuracy. The success of the decision tree method in classification was 92.6%. Conclusions: The machine learning algorithms can be used successfully in breast cancer diagnosis to determine whether the tumors are malign or benign.en_US
dc.identifier.doi10.18521/ktd.912462en_US
dc.identifier.endpage356en_US
dc.identifier.issn1309-3878
dc.identifier.issue2en_US
dc.identifier.startpage347en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12885/2014
dc.identifier.volume13en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorBilgin, Turgay Tugay
dc.language.isoenen_US
dc.publisherDüzce Üniversitesien_US
dc.relation.ispartofKONURALP TIP DERGISIen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBreast Cancer Diagnosisen_US
dc.subjectMachine Learningen_US
dc.subjectNaive Bayesen_US
dc.subjectDecision Treesen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectKNIMEen_US
dc.titleThe Investigation of the Success of Different Machine Learning Methods in Breast Cancer Diagnosisen_US
dc.typeArticleen_US

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