A hybrid classification algorithm approach for breast cancer diagnosis

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dc.contributor.authorAbed, Baraa M.
dc.contributor.authorShaker, Khalid
dc.contributor.authorJalab, Hamid A.
dc.contributor.authorShaker, Hothefa
dc.contributor.authorMansoor, Ali Mohammed
dc.contributor.authorAlwan, Ahmad F.
dc.contributor.authorAl-Gburi, Ihsan Salman
dc.date.accessioned2021-05-15T12:37:29Z
dc.date.available2021-05-15T12:37:29Z
dc.date.issued2016
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Temel Bilimler Bölümüen_US
dc.descriptionIEEE Conference on Industrial Electronics and Applications -- NOV 20-22, 2016 -- Kota Kinabalu, MALAYSIA
dc.descriptionMansoor, Ali Mohammed/0000-0003-2443-6637; Jalab, Hamid A./0000-0002-4823-6851; Shaker, Khalid/0000-0001-9108-5553
dc.description.abstractEarly diagnosis of Breast Cancer is significantly important to treat the disease easily therefore it is necessary to develop techniques that can help physicians to get accurate diagnosis. This study suggests a hybrid classification algorithm which is based upon Genetic Algorithm (GA) and k Nearest neighbor algorithm (kNN). GA algorithm has been used for its primary purpose as an optimization technique for kNN by selecting best features as well as optimization of the k value, while the kNN is used for classification purpose. The planned algorithm is tested by applying it on Wisconsin Breast Cancer Dataset from UCI Repository of Machine Learning Databases using different datasets in which the first is Wisconsin Breast Cancer Database (WBCD) and the second one is Wisconsin Diagnosis Breast Cancer (WDBC) which has changes in the number of attributes and number of instances. The proposed algorithm was measured against different classifier algorithms on the same database. The evaluation results of the algorithm proposed have achieved 99% accuracy.en_US
dc.description.sponsorshipIEEE Advancing Technology Humanity, IES, IAS IEEE Ind Application Soc, IEEE IE/IA Joint Chapter Malaysiaen_US
dc.description.sponsorshipUniversity of Malaya, MalaysiaUniversiti Malaya [RG312-14AFR]en_US
dc.description.sponsorshipThis research is supported by research grant RG312-14AFR from University of Malaya, Malaysia.en_US
dc.identifier.endpage274en_US
dc.identifier.isbn978-1-5090-0925-1
dc.identifier.issn2156-2318
dc.identifier.scopus2-s2.0-85034043514
dc.identifier.scopusqualityN/A
dc.identifier.startpage269en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/543
dc.identifier.wosWOS:000442445500043
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAl-Gburi, Ihsan Salman
dc.language.isoen
dc.publisherIeeeen_US
dc.relation.ispartof2016 Ieee Industrial Electronics and Applications Conference (Ieacon)
dc.relation.ispartofseriesIEEE Conference on Industrial Electronics and Applications
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBreast Cancer Diagnosisen_US
dc.subjectClassification Algorithmen_US
dc.subjectGenetic Algorithm And K Nearest Neighbor Algorithmen_US
dc.titleA hybrid classification algorithm approach for breast cancer diagnosis
dc.typeConference Object

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