Hematolojik Hastalıkların Tanısında Klonal Seçim Algortiması ile birlikte kullanılan K En yakın Komşu ve Regresyon Ağacı Sınıflandırıcılarının Mukayesesi

Yıl 2014, , 7 - 20, 01.07.2014

Burcu Çarklı Yavuz Tuba Karagül Yıldız Nilüfer Yurtay Ziynet Pamuk

https://doi.org/10.5824/1309-1581.2014.3.001.x

Öz

Bu çalışmanın amacı Hematolojik parametrelere bağlı sınıflandırma performansını artıracak bir yöntem geliştirmektir. Sınıflandırma problemlerinde Klonal Seçim Algoritması ile birlikte genellikle kNN sınıflandırıcısının kullanıldığı görülmüştür. Bu çalışmada diğer çalışmalardan farklı olarak kNN sınıflandırma algoritmasının yerine Gini algoritması uygulanmıştır ve daha yüksek başarı elde edilmiştir. Dünya sağlık örgütünün verilerine göre dünyadaki kadınların yaklaşık %10'u anemidir. Anemi hayat kalitesini düşüren ve tedavi edilmediğinde ciddi etkileri olan bir hastalıktır. Demir eksikliği anemisi aneminin en yaygın tipidir ve kadınlar erkeklere oranla bu hastalıktan daha fazla etkilenmektedir. Bu nedenle bu çalışmada örnek uygulama için anemi tercih edilmiştir. Hematolojik parametrelere bakarak tanı koyulan diğer hastalıklarda da önerilen metodun başarılı sonuçlar üreteceği beklenmektedir. Çalışmanın sonunda farklı yöntemlerle elde edilen başarı oranları ROC analizi yöntemiyle karşılaştırılmıştır. Bellek tabanlı sınıflandırıcı ile doğruluk oranı %96 olarak bulunurken Regresyon ağacı yöntemiyle doğruluk oranı %98.73 elde edilmiştir. Klonal Seçim Algoritmasında sınıflandırıcı olarak kNN yerine Gini algoritmasının kullanılması ile, Yapay Sinir Ağları metodlarından da daha yüksek başarı elde edilmiştir.

Anahtar Kelimeler

Bilgisayar Destekli Tanı, K En Yakın Komşu, Klonal Seçim Algoritması, Regresyon Ağaçları, Yapay Sinir Ağları

Comparison of K Nearest Neighbours And Regression Tree Classifiers Used With Clonal Selection Algorithm To Diagnose Haematological Diseases

Öz

The aim of this study is to develop a method to improve the classification performance by haematological parameters. In classification problems it has been seen that kNN classifier is often used with the clonal selection algorithm. In this study unlike other studies Gini algorithm is performed instead of kNN classification algorithm and higher success rate is obtained. According to the World Health Organisation’s data nearly 10% of women in the world are anaemia. Anaemia is a disease that disrupts life quality and results in serious effects if not cured. Iron deficiency anaemia is the most common type of anaemia and women suffers this disease comparatively to men. Therefore, in this study, anaemia was preferred as a sample application. It is expected to reach successful results in diagnosis of other diseases by looking at haematological parameters with the proposed method. At the end of the study success ratios of different methods are compared by Receiver Operating Characteristics analysis method. While accuracy in memory-based classification is found as 96%, accuracy in regression tree method classification is 98.73%. Using Gini algorithm instead of kNN a higher success ratio is achieved so CSA surpassed ANN’s success ratio.

Anahtar Kelimeler

Artificial Neural Network, Clonal Selection Algorithm, Computer Aided Diagnosis, K-Nearest Neighbours, Regression Trees.

Kaynakça

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