ASSOCIATION RULE FOR CLASSIFICATION OF BREAST CANCER PATIENTS

dc.contributor.authorPala, Tuba
dc.contributor.authorYücedağ, İbrahim
dc.contributor.authorBiberoğlu, Hasan
dc.date.accessioned2020-04-30T22:39:56Z
dc.date.available2020-04-30T22:39:56Z
dc.date.issued2017
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.descriptionWOS: 000416216800009en_US
dc.description.abstractData mining studies carried out on medical databases are very important in order to make an effective medical diagnosis. The purpose of data mining is to extract information from databases, to define clear and understandable patterns. In this study, an approach was presented to generate association rules on the data of breast cancer patients. Apriori algorithm is used for the extract of the rules. Apriori algorithm is usually used for the market - basket analysis. Apriori algorithm is used to determine customer shopping profiles or to campaign, in order to catch the shopping patterns. In this study, apriori algorithm was used in the extraction of the rules within the medical data. UC-Irvine archive repository of machine learning datasets [1] - Breast Cancer dataset has been studied. This dataset, including 9 attribute and 1 class atribute. It consists of records of 286 patients with 10 attributes. The study was carried out by using the Weka data mining program.en_US
dc.identifier.endpage160en_US
dc.identifier.issn1304-7205
dc.identifier.issn1304-7191
dc.identifier.issue2en_US
dc.identifier.startpage155en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12684/2864
dc.identifier.volume8en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherYildiz Technical Univen_US
dc.relation.ispartofSigma Journal Of Engineering And Natural Sciences-Sigma Muhendislik Ve Fen Bilimleri Dergisien_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectData miningen_US
dc.subjectclassificationen_US
dc.subjectassociation ruleen_US
dc.subjectbreast cancer dataseten_US
dc.subjectmedical diagnosisen_US
dc.titleASSOCIATION RULE FOR CLASSIFICATION OF BREAST CANCER PATIENTSen_US
dc.typeArticleen_US

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