An Application on the Use of Explainable Artificial Intelligence in the Healthcare

Yıl 2025, Cilt: 40 Sayı: 3, 707 - 723, 26.09.2025

Gülhan Toğa

https://doi.org/10.21605/cukurovaumfd.1729986

Öz

With the advent of big data, the potential to derive valuable insights has significantly increased. The widespread use of machine learning, especially deep learning, has made it possible to extract meaningful patterns from large datasets. However, end-users often encounter issues related to the transparency, interpretability, and reliability of these models. To address such concerns, Explainable Artificial Intelligence (XAI) has been developed to systematically clarify how models work and how their decisions can be understood and trusted.
This study explores the use and benefits of XAI in the healthcare domain. As an example, it presents an application that predicts the survival status (alive or deceased) of breast cancer patients based on various clinical parameters. Initially, classification was performed using machine learning algorithms. To interpret the results of the most successful model, the SHAP (SHapley Additive exPlanations) technique, a prominent XAI method, was applied. Findings indicate that XAI enhances understanding of model decisions and supports trustworthy AI applications in healthcare.

Anahtar Kelimeler

Explainable Artificial Intelligence , SHAP , Data Mining , Breast Cancer

Sağlık Alanında Açıklanabilir Yapay Zekâ Kullanımı Üzerine Bir Uygulama

Öz

Büyük verilerin hayatımıza girmesi ile bu verilerle elde edilebilecek bilgiler de artmıştır. Özellikle derin öğrenmeye yönelik olmak üzere makine öğreniminin yaygın kullanımı sayesinde büyük veri kümelerinden anlamlı çıkarımlar yapabilmek mümkün hale gelmiştir. Ancak son kullanıcılar, oluşturulan modellerin şeffaflık, yorumlanabilirlik ve güvenilirlik gibi bazı eksiklikleri ile karşı karşıya kalmaktadır. Bu eksikliklerin giderilmesi için geliştirilen “Açıklanabilir Yapay Zekâ (AYZ)”, sistematik bir yaklaşım ile oluşturulan modelin ve alınan kararların kullanıcılar tarafından anlaşılmasını ve modellerin güvenilirliğinin nasıl değerlendirilmesi gerektiğini açıklayabilmektedir.
Bu çalışmada sağlık alanında AYZ kullanımı ve faydaları hakkında bilgi verilmektedir. Ayrıca çalışma, sağlık alanında AYZ kullanımına örnek olması açısından, meme kanseri hastalarının çeşitli parametrelere bağlı olarak sağ/ölü durumlarının öngörülmesine olanak sağlayan bir uygulama içermektedir. Öncelikle, makine öğrenmesi algoritmaları kullanılarak sınıflandırma çalışması yapılmıştır. Ardından en başarılı sınıflandırma modelinin sonuçlarının açıklanabilirliği için bir AYZ yöntemi olan SHAP tekniğine başvurulmuştur. Sonuçlar, AYZ kullanımının modelin karar verme sürecinin nedenlerini daha iyi anlamamıza yardımcı olduğunu göstermektedir.

Anahtar Kelimeler

Açıklanabilir Yapay Zeka , Veri Madenciliği , Meme Kanseri , SHAP

Kaynakça

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