BREAST CANCER DIAGNOSIS WITH FEATURE SELECTION USING NATURE-INSPIRED OPTIMIZATION AND MACHINE LEARNING

Abstract

Breast cancer is the most common type of cancer in women and is the leading cause of death. If diagnosed and treated early, the rate of recovery and survival increases. Machine learning, with different applications in the medical field, plays a successful decision support role for experts in the early diagnosis of cancer types. Using all of the many features collected to diagnose a disease can negatively affect the analysis process and success. Selecting the most effective features from the collected data and making predictions using them can increase the success of the diagnosis. In this study, classifications were carried out on the UCI WDBC dataset, which is widely used in the literature on breast cancer, with KNN, RF, and SVM, without feature selection and by applying feature selection. Feature selection operations were performed on the original WDBC dataset containing 30 features, using nature-inspired algorithms Cuckoo Search (CS), Particle Swarm Optimization (PSO), Whale Optimization (WOA), and Red Deer Algorithm (RDA) consisting of 25, 50, and 75 particles. The highest accuracy was obtained with the RO classifier as 99.12%, using 16 features selected with 75-particle CS. It was observed that the accuracy of the classifications made by feature selection was higher than the results obtained without feature selection. The findings were compared with the current studies in the literature and it was observed that it provided higher success.

Keywords

• Breast Cancer Diagnosis
• Nature-Inspired Optimization
• Feature Selection
• Machine Learning.

DOĞA İLHAMLI OPTİMİZASYON KULLANARAK ÖZELLİK SEÇİMİ VE MAKİNE ÖĞRENMESİ İLE MEME KANSERİ TEŞHİSİ

Abstract

Meme kanseri kadınlarda en yaygın görülen kanser türü ve en çok ölüme sebep olan hastalıktır. Erken teşhis ve tedavi edilirse iyileşme ve hayatta kalma oranı yükselmektedir. Makine öğrenmesi, medikal alanda farklı uygulamalarıyla kanser türlerinin erken teşhisinde uzmanlar için başarılı bir karar destek rolü oynamaktadır. Bir hastalığın tanısı için toplanan çok sayıda özelliğin tümünün birlikte kullanımı analiz süreci ve başarısını olumsuz etkileyebilmektedir. Toplanan verilerden en etkin özniteliklerin seçilmesi ve bunları kullanarak tahminlemeler yapılması teşhis başarısını artırabilmektedir. Bu çalışmada meme kanseri konusunda literatürde yaygın kullanılan UCI WDBC veri seti üzerinde KNN, RO ve DVM algoritmaları ile öznitelik seçimi olmadan ve öznitelik seçimi uygulanarak sınıflamalar gerçekleştirilmiştir. Öznitelik seçimi konusunda başarılı sonuçlar sağlayan doğa ilhamlı algoritmalardan Guguk Kuşu Arama (GKA), Parçacık Sürü Optimizasyonu (PSO), Balina Optimizasyon (BO) algoritması ve Kızıl Geyik (KG) algoritması kullanılarak ve bu algoritmaların 25,50 ve 75 parçacıklı sürüleri ile 30 öznitelikli orijinal WDBC veri seti üzerinde öznitelik seçimleri gerçekleştirilmiştir. En yüksek doğruluk 75 parçacıklı GKA ile seçilen 16 adet öznitelik ile RO sınıflandırıcı kullanılarak %99.12 olarak elde edilmiştir. Öznitelik seçimi yapılarak gerçekleştirilen sınıflamalardaki doğruluklarının, seçim yapılmadan elde edilen sonuçlardan yüksek olduğu görülmüştür. Eldeki bulgular literatürdeki çalışmalar ile karşılaştırılmış ve daha yüksek başarı sağladığı gözlenmiştir.

Keywords

• Meme Kanseri Teşhisi
• Doğa İlhamlı Optimizasyon
• Özellik Seçimi
• Makine Öğrenmesi.

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