Enhancing Early Breast Cancer Diagnosis with MRMR and GRU-based Model

Yıl 2024, Cilt: 24 Sayı: 2, 320 - 329, 29.04.2024

Samet Aymaz

https://doi.org/10.35414/akufemubid.1360156

Öz

Breast cancer is one of the most common cancers in women worldwide and early detection can be life-saving. This study aims to develop an accurate and reliable model for breast cancer diagnosis by focusing on the Wisconsin Breast Cancer Diagnosis (WDBC) dataset. In the first stage, feature selection was performed using the Minimum Redundancy Maximum Relevance (MRMR) method. The method is used as an effective tool in the field of data mining and feature selection. With MRMR, the importance of the features is ranked and only the significant ones are used. Feature selection improves performance while reducing the complexity of the model. Then, these features selected by MRMR are classified by a Gated Recurrent Unit (GRU) based neural network model for breast cancer classification. The GRU is capable of handling one-dimensional feature series and gives effective results in complex classification problems. The results show that this innovative approach is highly successful in breast cancer diagnosis. In the evaluations, 98.28% for accuracy metric, 98.59% for precision metric, 98.59% for sensitivity metric, 97.67% for specificity metric and 98.59% for f-score metric were obtained. The results show that the method can help specialists in clinical practice. It is understood from the results that the proposed approach has important advantages such as accessibility for all segments of the society, fast and high accuracy even in simple systems.

Anahtar Kelimeler

Breast cancer, MRMR method, GRU model, Feature selection

Meme Kanseri Erken Teşhisi için MAMA ve KTB Kullanarak Geliştirilen Model

Öz

Meme kanseri, dünya genelinde kadınlarda en sık görülen kanser türlerinden biridir ve bu hastalıkta erken teşhis hayat kurtarıcı olabilir. Bu çalışma, Wisconsin Meme Kanseri Teşhisi (WMKT) veri setine odaklanarak meme kanseri teşhisi için doğru ve güvenilir bir model geliştirme amacı gütmektedir. Çalışmada, ilk aşamada Minimum Artıklık Maksimum Alaka Düzeyi (MAMA) yöntemi kullanılarak özellik seçimi yapılmıştır. Yöntem, veri madenciliği ve özellik seçimi alanında etkili bir araç olarak kullanılmaktadır. MAMA ile özelliklerin önem sıralaması yapılarak, sadece anlamlı olanlar kullanılmıştır. Özellik seçimi, modelin karmaşıklığını azaltırken performansı artırır. Daha sonra, MAMA ile seçilen bu özellikler, meme kanseri sınıflandırması için oluşturulan Kapılı Tekrarlayan Birim (KTB) tabanlı bir sinir ağı modeli ile sınıflandırılmaktadır. KTB, tek boyutlu özellik serilerini işleme yeteneğine sahiptir ve karmaşık sınıflandırma problemlerinde etkili sonuçlar verir. Sonuçlar, bu yenilikçi yaklaşımın meme kanseri teşhisinde oldukça başarılı olduğunu göstermektedir. Yapılan değerlendirmelerde doğruluk metriği için %98.28, kesinlik metriği için %98.59, duyarlık metriği için %98.59, özgüllük metriği için %97.67 ve F-puanı metriği için %98.59 değerleri elde edilmiştir. Sonuçlar yöntemin klinik uygulamalarda uzmanlara yardımcı olabileceğini ortaya koymaktadır. Önerilen yaklaşımın toplumun her kesimi için erişilebilirlik, basit sistemlerde bile hızlı ve yüksek doğrulukla çalışabilmek gibi önemli avantajları olduğu sonuçlardan anlaşılmaktadır.

Anahtar Kelimeler

Meme kanseri, Özellik seçimi, MAMA yöntemi;, KTB modeli

Kaynakça

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