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.

Öznitelik Seçimi ile Desteklenen Makine Öğrenmesine Dayalı Göğüs Kanserinin Erken Tespiti ve Teşhisi

“…Klinik sayısal bulguların yer aldığı bir veri kümesi kullanılmıştır. Bu çalışmada farklı yöntemler denenmiş olup K en yakın komşu algoritmasının en yüksek başarıyı gösterdiği belirtilmiştir K en yakın komşu algoritması ile yapılan sınıflandırmada %99,42 doğruluk oranı elde edilmiştir [10]. Bozkurt Keser ve Keskin, tarafından sunulan çalışmada göğüs kanseri sınıflandırılmıştır.…”