Prediction of Breast Cancer Using Artifical Neural Networks

Öz

Breast cancer is one of the most important malignant diseases in the world. In the United States, breast cancer ranks first among all oncological diseases in women and is the second leading cause of cancer mortality after lung cancer. Despite recent great success in the early detection and treatment of breast cancer, new approaches and algorithms are still being developed for early diagnosis. Breast cancer has many classifications, like other malignant diseases: histological, molecular, functional, TNM classification. Most cases of cancer can be diagnosed in the later stages of the disease, and treatment is often not responding and the patient is lost. Therefore, early detection of breast cancer is vital. This study uses the UCI Breast Cancer Wisconsin (Diagnostic) Data Set (WDBC), which is presented by measuring test classification accuracy, sensitivity, and specificity values. The data set was divided into 70% for the training phase and 30% for the testing phase. This study demonstrates the importance of optimization algorithm selectiona and parameters in the diagnosis of Breast Cancer using Artificial Neural Networks and investigates how they should be chosen. The accuracy results of different optimization algorithms and parameter values are reported.

Anahtar Kelimeler

• Artificial Neural Networks,Breast Cancer,Breast Cancer Diagnosis

Yapay Sinir Ağı Kullanarak Meme Kanseri Hastalığının Tahmini

Öz

Günümüzde meme kanseri (breast cancer) dünyadaki en önemli kötü huylu hastalıklardan biridir. ABD'de meme kanseri, kadınlarda tüm onkolojik hastalıklar arasında birinci sırada yer alır ve akciğer kanserinden sonra onkolojide ölüm nedeninin ikincisidir. Meme kanserinin erken teşhisinde ve tedavisinde son zamanlarda elde edilen büyük başarılara rağmen, ilk aşamalarda teşhisi için yeni yaklaşımlar ve algoritmalar geliştirilmeye devam etmektedir. Meme kanseri, diğer kötü huylu hastalıklar gibi birçok sınıflandırmaya sahiptir. Histolojik, moleküler, fonksiyonel, TNM sınıflandırması bunlardan bazılarıdır. Çoğu kanser vakası hastalığın geç aşamalarında ancak teşhis edilebilir ve tedavi sıklıkla cevap vermez ve hasta kaybedilir. Bu sebepten meme kanserinin erken evrelerde teşhisi hayati önem taşır. Bu çalışmada sınıflandırma testi doğruluğunu, hassasiyet ve özgüllük değerlerini ölçerek sunmakta olan Wisconsin Meme Kanseri Teşhisi (WDBC) veri seti kullanılmaktadır. Uygulamada, veri seti eğitim aşaması için %70 ve test aşaması için %30 olarak bölünmüştür. Bu çalışma yapay sinir ağı kullanarak meme kanseri tahmininde optimizasyon algoritmalarının ve parametrelerin nasıl seçilmesi gerektiğini incelemekte ve farklı seçimlerinin nasıl sonuç verdiğini göstermektedir.

Anahtar Kelimeler

• Yapay Sinir Ağları,Meme Kanseri,Meme Kanseri Tahmini

Kaynakça

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