Using Five Machine Learning for Breast Cancer Biopsy Predictions Based on Mammographic Diagnosis

dc.contributor.authorOyewola, David
dc.contributor.authorHakimi, Danladi
dc.contributor.authorAdeboye, Kayode
dc.contributor.authorShehu, Musa Danjuma
dc.date.accessioned2018-12-11T12:44:58Z
dc.date.available2018-12-11T12:44:58Z
dc.date.issued2017-04-20
dc.descriptionDOI: 10.19072/ijet.280563en_US
dc.description.abstractBreast cancer is one of the causes of female death in the world. Mammography is commonly used for distinguishing malignant tumors from benign ones. In this research, a mammographic diagnostic method is presented for breast cancer biopsy outcome predictions using five machine learning which includes: Logistic Regression(LR), Linear Discriminant Analysis(LDA), Quadratic Discriminant Analysis(QDA), Random Forest(RF) and Support Vector Machine(SVM) classification. The testing results showed that SVM learning classification performs better than other with accuracy of 95.8% in diagnosing both malignant and benign breast cancer, a sensitivity of 98.4% in diagnosing disease, a specificity of 94.4%. Furthermore, an estimated area of the receiver operating characteristic (ROC) curve analysis for Support vector machine (SVM) was 99.9% for breast cancer outcome predictions, outperformed the diagnostic accuracies of Logistic Regression(LR), Linear Discriminant Analysis(LDA), Quadratic Discriminant Analysis(QDA), Random Forest(RF) methods. Therefore, Support Vector Machine (SVM) learning classification with mammography can provide highly accurate and consistent diagnoses in distinguishing malignant and benign cases for breast cancer predictions.en_US
dc.identifier.issn2149-0104
dc.identifier.issn2149-5262
dc.identifier.urihttps://hdl.handle.net/11363/525
dc.language.isoenen_US
dc.publisherİstanbul Gelişim Üniversitesi Yayınları / Istanbul Gelisim University Pressen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Yayınıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectResearch Subject Categories::TECHNOLOGYen_US
dc.titleUsing Five Machine Learning for Breast Cancer Biopsy Predictions Based on Mammographic Diagnosisen_US
dc.typeArticleen_US

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