Analysis of Mutated RNA-Type Breast Cancer Data with Machine Learning Methods

Year 2021, Volume: 16 Issue: 2, 251 - 260, 15.09.2021

Rumeysa Hanife Kars

Abstract

According to the data for the year 2020, the three most common types of cancer in women are; breast, lung, and colorectal. These types of cancer make up 50% of other types of cancer seen in women. In addition, only breast cancer accounts for 30% of cancer types in women. Early diagnosis and treatment processes of breast cancer patients are important and the correct application of this process increases the survival rate of the patients. Artificial intelligence can contribute to the observational performance of radiologists in breast cancer screening. On the other hand, artificial intelligence-based approaches can also be used to increase the accuracy of digital mammography. The dataset used in this study consists of mutated RNA-type breast cancer data. The data set includes the clinical and genetic characteristics of the patients. In the approach of the study, it is suggested to use various machine learning methods together. Support Vector Machines method has been decided the best performance with 97.55% in the analyzes performed. It has been observed that the recommended approach in the diagnosis of breast cancer gave successful results.

Keywords

Artificial intelligence, Breast cancer, Machine learning, Mutation RNA

Mutasyona Uğramış RNA tipli Göğüs Kanseri Verilerinin Makine Öğrenme Yöntemleri ile Analizi

Abstract

Kadınlarda görülen en yaygın üç kanser türü 2020 yılı verilerine göre; göğüs, akciğer ve kolorektaldır. Bu kanser türleri kadınlarda görülen diğer kanser türleri arasında %50'sini oluşturmaktadır. Ayrıca, kadınlarda görülen kanser türleri arasında yalnızca göğüs kanseri %30'unu oluşturmaktadır. Göğüs kanseri hastalarının, erken tanı ve tedavi süreçleri önemlidir ve bu sürecin doğru uygulanması hastaların hayatta kalma oranlarını artırır. Yapay zekâ, radyologların göğüs kanseri taramasındaki gözlemleme performanslarına katkı sağlayabilir. Öte yandan yapay zekâ tabanlı yaklaşımlar, dijital mamografinin doğruluğunu artırmak için de kullanılabilir. Bu çalışmada kullanılan veri kümesi mutasyona uğramış RNA tipi göğüs kanseri verilerinden oluşur. Veri kümesinde hastaların klinik özellikleri ile genetik özellikleri yer alır. Çalışmanın yaklaşımında çeşitli makine öğrenimi yöntemlerinin bir arada kullanılması önerilmiştir. Gerçekleştirilen analizlerde en iyi performansı %97,55 oranında Destek Vektör Makineleri yöntemi verdi. Göğüs kanseri tanısında önerilen yaklaşımın başarılı sonuçlar verdiği gözlemlenmiştir.

Keywords

Göğüs kanseri, Makine öğrenme, Mutasyon RNA, Yapay zekâ

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