Breast Cancer Diagnosis with Weighted Vote Based Ensemble Classification Algorithm

Year 2022, Volume: 4 Issue: 2, 112 - 120, 26.10.2022

Sinem Bozkurt Keser , Kemal Keskin

https://doi.org/10.46387/bjesr.1092607

Cited By: 2

Abstract

Breast cancer is a disease that is among the second causes of death among women, but its fatal risk is reduced with early diagnosis and the right treatment method. Currently, a large number of classification algorithms in data mining fields are adapted to breast cancer diagnosis based on patients' past medical records. With the help of these algorithms, the accuracy of diagnosis in diseases is significantly increased. In this study, a weighted vote-based ensemble classification algorithm is proposed for the diagnosis of breast cancer. The proposed algorithm is based on the working principle of more than one classification algorithm. By combining the classification algorithms with the weighted voting method, the result obtained from each algorithm alone is improved. The proposed weighted vote-based community classification algorithm consists of four stages. The first stage is the data preprocessing stage, followed by the classification stage. In the third stage, the reclassification process is carried out by using the weighted vote-based community classification algorithm with the performance values obtained from the classification process. With the proposed algorithm, an accuracy value of %98.77 was obtained, and a better value was obtained than the individual performance of each classification algorithm used in the classification phase.

Keywords

Breast cancer diagnosis, machine learning, classification, ensemble methods, voting mechanism

Ağırlıklı Oy Tabanlı Topluluk Sınıflandırma Algoritması ile Göğüs Kanseri Teşhisi

Abstract

Meme kanseri, kadınlar arasında ikinci ölüm nedenleri arasında gösterilen fakat erken teşhis ve ardından uygulanan doğru tedavi yöntemi ile ölümcül riski azaltılan bir hastalıktır. Günümüzde, veri madenciliği alanlarındaki çok sayıda sınıflandırma algoritması, hastaların geçmiş tıbbi kayıtlarına dayalı olarak meme kanseri teşhisine uyarlanmaktadır. Bu algoritmaların yardımı ile hastalıklardaki teşhis doğruluğu önemli ölçüde artırılmaktadır. Bu çalışmada, meme kanseri tanısı için ağırlıklı oy tabanlı topluluk sınıflandırma algoritması önerilmektedir. Önerilen algoritma, birden fazla sınıflandırma algoritmasının bir arada çalışma prensibine dayanmaktadır. Sınıflandırma algoritmaları ağırlıklı oylama yöntemi ile bir araya getirilerek her bir algoritmadan tek başına elde edilen sonucun iyileştirilmesi sağlanmaktadır. Önerilen ağırlıklı oy tabanlı topluluk sınıflandırma algoritması dört aşamadan oluşmaktadır. İlk aşama veri önişleme aşaması olup bu aşamayı sınıflandırma aşaması izlemektedir. Üçüncü aşamada, sınıflandırma işleminden elde edilen performans değerleri ile ağırlıklı oy tabanlı topluluk sınıflandırma algoritması kullanılarak yeniden sınıflandırma işlemi gerçekleştirilmektir. Önerilen algoritma ile %98.77 doğruluk değeri elde edilerek sınıflandırma aşamasında kullanılan her bir sınıflandırma algoritmasının bireysel performansından daha iyi bir değer elde edilmiştir.

Keywords

Meme kanseri teşhisi, makine öğrenmesi, sınıflandırma, topluluk yöntemleri, oylama mekanizması.

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