Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
Gail Modeli ile Makine Öğrenmesi Algoritmalarının Meme Kanseri Risk Değerlendirmesinde Karşılaştırılması: Metodolojik Çalışma
Comparison of the Machine Learning Algorithms in Breast Cancer Risk Assessment with the Gail Model: Methodological Study
Berfu PARÇALIa, Fezan MUTLUa
aEskişehir Osmangazi Üniversitesi Sağlık Bilimleri Enstitüsü, Biyoistatistik ABD, Eskişehir, Türkiye
Turkiye Klinikleri J Biostat. 2022;14(1):32-44
doi: 10.5336/biostatic.2021-85755
Article Language: TR
Full Text
ÖZET
 Amaç: Meme kanserinin; meme dokusu içiresinde yer alan süt kanallarının doku hücrelerinde oluştuğu bilinmektedir. Süt kanallarını oluşturan bu hücrelerin kontrolsüz olarak artmasına ise duktal hiperplazi denir. Bir kadında, yaşamı süresince invazif (yayılma eğilimi olan) meme kanseri gelişme riskinin %13,3 olduğu bilinmektedir. Meme kanserinin oluşma riski yaşa bağlı olarak artmaktadır. Gail modeli; meme kanserinde temel faktörleri değerlendiren, genel olarak kabul görmüş kanser riski değerlendirme modelidir. Bu çalışmada, Gail modeli baz alınarak, makine öğrenmesi yöntemlerinin meme kanseri risk değerlendirmesinde karşılaştırılması amaçlanmıştır. Gereç ve Yöntemler: İlk olarak, veri setine Gail modeli uygulanmış ve risk faktörü belirlenmiş ve %80 eğitim, %20 test olmak üzere ayrı eğitim test veri seti oluşturulmuştur. Daha sonra oluşturulan bu veri setlerine k-en yakın komşu, yapay sinir ağları (YSA), destek vektör makinesi [support vector machine (SVM)] ve naive Bayes (NB) algoritmaları uygulanmış ve uygukanan yöntemlerin risk tahmin sonuçları karşılaştırılmıştır. Bulgular: Karşılaştırma sonuçlarına göre %80 eğitim, %20 test veri seti için sınıflandırma performansı en düşükten en yükseğe doğru sırasıyla SVM [eğri altında kalan alan (area under the curve 'AUC') =0,911], NB (AUC=0,939) ve YSA (AUC=0,949) şeklindedir. Sonuç: Meme kanserinin erken aşamada teşhis edilmesi; tedavi yöntemlerinin sayısını, tedavinin başarıya ulaşma oranını ve hayatta kalma şansını artırmaktadır. Meme kanseri risk hesaplamasında makine öğrenmesi yöntemlerinin etkili olduğu görülmüştür.

Anahtar Kelimeler: Gail modeli; meme kanseri; makine öğrenmesi; yapay sinir ağları; destek vektör makinesi
ABSTRACT
 Objective: Breast cancer; it occurs in the tissue cells of the milk ducts in the breast tissue. The uncontrolled increase in the cells forming the milk ducts is called ductal hyperplasia. It is known that the risk of developing invasive (with a tendency to spread) breast cancer in a woman during her lifetime is 13.3%, and the risk of developing breast cancer increases with age. The Gail model is a well accepted cancer risk assessment model which evaluates the main factors in breast cancer. The aim of this study is to compare machine learning methods in breast cancer risk assessment based on the Gail model. Material and Methods: Firstly, the risk factor was determined by the application of the Gail model into the data set, discrete training test data sets were presented which is 80% train and 20% test. Afterwards, k-nearest neighbor, artificial neural network (ANN), support vector machine (SVM) and naive Bayes (NB) algorithms applied on each set and risk estimation results were compared. Results: Classification performance from the lowest to the highest for 80% training and 20% test data set according to the comparison results is as follows; SVM [area under the curve (AUC)=0.911], NB (AUC=0.939) and ANN (AUC=0.949). Conclusion: Early diagnosis of breast cancer increases the number of possible treatments, the success rate of the treatments and the chance of survival. It has been seen that machine learning algorithms effective in breast cancer risk calculation.

Keywords: Gail model; breast cancer; machine learning; artificial neural network; support vector machine
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