ASSOCIATION RULE FOR CLASSIFICATION OF BREAST CANCER PATIENTS
Küçük Resim Yok
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Yildiz Technical Univ
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Data 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.
Açıklama
WOS: 000416216800009
Anahtar Kelimeler
Data mining, classification, association rule, breast cancer dataset, medical diagnosis
Kaynak
Sigma Journal Of Engineering And Natural Sciences-Sigma Muhendislik Ve Fen Bilimleri Dergisi
WoS Q Değeri
N/A
Scopus Q Değeri
Cilt
8
Sayı
2
