Makine Öğrenimi Teknikleri ile Göğüs Kanserinin Teşhisi

Year 2022, , 594 - 603, 30.06.2022

Halime Doğan , Ahmet Tatar , Alper Kadir Tanyıldızı , Beyda Taşar

https://doi.org/10.17798/bitlisfen.1065685

Cited By: 1

Abstract

Kanser ölümleri en yüksek oranlı ölüm nedenlerinden biridir. Göğüs kanseri kadınlara özgü olduğu sanılsa da erkeklerde de yaygın olarak görülmekte ve erkeklerde görülen göğüs kanserinde ölüm oranı daha yüksek olabilmektedir. Göğüs kanseri hastalığında erken teşhis ve tedavi çok önemlidir. Uzman sistemler, yapay zekâ ve makine öğrenmesi teknikleri ile kanserin erken evrede teşhisine imkân sağlanmakta ve veri analizleri ile sağlık personellerine kolaylık sunulmaktadır. Bu çalışmada en yakın komşu algoritması, temel bileşen analizi ve komşuluk bileşenleri analizi teknikleri kullanılarak göğüs kanserinin tespiti çalışması gerçekleştirilmiştir. Geliştirilen yöntem “Breast Cancer Wisconsin Diagnostic” veri seti kullanılarak geliştirilmiş ve test edilmiştir. Elde edilen sonuçlara göre en yüksek başarı oranı %99.42 ile komşuluk bileşen analizi ve en yakın komşu sınıflandırma algoritması yöntemi kullanılarak elde edilmiştir.

Keywords

Makine Öğrenimi, Göğüs Kanseri, Wisconsin Veri seti, Sınıflandırma.

Breast Cancer Diagnosis with Machine Learning Techniques

Abstract

Cancer deaths are one of the highest rates of death. Although breast cancer is commonly associated with women, it is sometimes seen in men, and the mortality rate for men with breast cancer may be higher. The importance of early detection and treatment of breast cancer cannot be overstated. Cancer is diagnosed at an early stage thanks to expert systems, artificial intelligence, and machine learning approaches, and data analysis makes life easier for healthcare professionals. The nearest neighbor method, principal component analysis, neighborhood component method approaches were employed to detect breast cancer in this study. "Breast Cancer Wisconsin Diagnostic" database was used to create and test the approach. According to the results obtained, the highest success rate with 99.42% was obtained by using neighborhood component analysis and nearest neighbor classification algorithm method.

Keywords

Machine Learning, Breast Cancer, Wisconsin DataSet, Classification.

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