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MEME KANSERİ SINIFLANDIRMASI İÇİN VERİ FÜZYONU VE GENETİK ALGORİTMA TABANLI GEN SEÇİMİ

GENE SELECTION FOR BREAST CANCER CLASSIFICATION BASED ON DATA FUSION AND GENETIC ALGORITHM

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Abstract (2. Language): 
Early diagnosis of breast cancer has been playing very important role on treatment of the disease. In this work, a new feature selection method for breast cancer classification based on data fusion and genetic algorithm is presented. The study consists of two steps: In the first step, the dimensionality of the gene expression dataset was reduced with filter method and the second step, significant genes have been identified with genetic algorithm. SVM was used for fitness function in genetic programming. In this study the classification accuracy rate was obtained 94.65 % when using selected 10 genes.
Abstract (Original Language): 
Ciddi rahatsızlıklardan biri olan meme kanserinin erken teşhis edilmesi hastalığın tedavisinde önemli rol oynar. Bu çalışmada meme kanseri hastalığında etkin rol oynayan genlerin belirlenmesi için veri füzyonu ve genetik algoritma tabanlı yeni bir nitelik seçme metodu önerilmektedir. Yapılan çalışma iki aşamadan oluşmaktadır: İlk aşamada filtreleme yöntemi ile gen ifade verisi indirgenmiş, ikinci aşamada genetik algoritma ile meme kanserinde etkin rol alan genlerin tespiti gerçekleştirilmiştir. Destek vektör makinesi, genetik algoritma için uygunluk fonksiyonu olarak kullanılmıştır. Yapılan çalışmada belirlenen 10 gen ile sınıflandırma doğruluk oranı %94,65 elde edilmiştir.
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