Farklı Yöntemler Kullanarak Meme Kanseri Teşhisinin Uygulamalı Bir Analizi

Yıl 2022, Cilt: 2 Sayı: 2, 72 - 87, 21.12.2022

İclal Çetin Taş

Öz

Meme kanseri dünyanın her bölgesinde en sık görülen kanser türlerinden biridir. Meme kanserinden ölümler her yıl katlanarak artıyor. Tüm kanser türlerinde olduğu gibi meme kanserinde de erken teşhis önemlidir ve birçok kez hayat kurtarır. Bu nedenle erken tanıyı kolaylaştırmak veya hastalığı erken öngörmek için birçok çalışma yapılmaktadır. Tahmin uygulamalarında kullanılan yöntemlerin başında makine öğrenmesi yöntemleri gelmektedir. Bu çalışmada, genel regresyon sinir ağları (GRNN), radyal temel fonksiyon (RBF), karar ağacı ormanı (DTF) ve gen ekspresyon programlaması (GEP), Meme Kanseri Wisconsin Diagnostic veri seti üzerinde analiz edilmiştir. Elde edilen sonuçlara göre makine öğrenmesi algoritmaları kullanılarak meme kanserinin erken teşhisine katkı sağlamak için sınıflandırıcılar arasında performans değerlendirmesi ve karşılaştırma yapılmıştır. En doğruluk GRNN algoritmasından elde edilir, %98.8'dir.

Anahtar Kelimeler

Makine öğrenmesi, Meme kanseri, Sınıflama, Tahminleme, Teşhis.

An Applied Analysis of Breast Cancer Diagnosis By Using Different Methods

Öz

Breast cancer is one of the most common cancer types in every region of the world. Deaths from breast cancer are increasing exponentially every year. As with all cancer types, early diagnosis is important in breast cancer and saves lives many times over. For this reason, many studies are carried out to facilitate early diagnosis or to predict the disease early. Machine learning methods are at the forefront of the methods used in prediction applications. In this study, general regression neural networks (GRNN), radial basis function (RBF), decision tree forest (DTF) and gene expression programming (GEP) were analyzed on the Breast Cancer Wisconsin Diagnostic dataset. According to the results obtained, a performance evaluation and comparison were made between the classifiers to contribute to the early diagnosis of breast cancer by using machine-learning algorithms. The best accuracy was obtained from the GRNN algorithm, it is 98.8%.

Anahtar Kelimeler

Breast cancer, Classification, Diagnosis, Machine learning, Prediction.

Kaynakça

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