ÇOK KATMANLI PERSEPTRON SİNİR AĞLARI İLE DİYABET HASTALIĞININ TEŞHİSİ

Yıl 2006, Cilt: 21 Sayı: 2, 319 - 326, 01.03.2013

İnan Güler Elif Übeyli

Öz

Yapay sinir ağları (YSA), farklı disiplinlerdeki karmaşık problemlerin çözümlenmesinde kabul gören ve uygulamalarda sıklıkla yer alan modelleme araçları haline gelmiştir. Farklı YSA yapıları, tıp alanında karar destek sistemlerinin gelişmesinde kullanılmakta olan önemli modellerdendir. Bu çalışmada, dört farklı algoritma ile eğitilen çok katmanlı perseptron sinir ağları diyabet hastalığının teşhisinde kullanılmış ve en başarılı algoritma belirlenmiştir. Geri yayılım, delta-bar-delta, genişletilmiş delta-bar-delta ve hızlı yayılım, çalışılmış olan dört algoritmadır. Çok katmanlı perseptron sinir ağlarının eğitimi, geçerliliği ve testi veri tabanında yer alan farklı kişilere ait kayıtlar ile yapılmıştır. Performans belirleyiciler ve istatistiksel ölçümler ile çok katmanlı perseptron sinir ağları değerlendirilmiş ve sonuçlar diyabet hastalığının teşhisinde hızlı yayılım algoritmasının en
başarılı çok katmanlı perseptron eğitim algoritması olduğunu göstermiştir.

Anahtar Kelimeler

Çok katmanlı perseptron sinir ağı , eğitim algoritmaları , diyabet hastalığının teşhisi.

DIABETES DIAGNOSIS BY MULTILAYER PERCEPTRON NEURAL NETWORKS

Öz

Artificial neural networks (ANNs) have become modeling tools that have found extensive acceptance and they
have frequently used in applications in many disciplines for solving complex problems. Different ANN
structures are valuable models, which are used in the medical field for the development of decision support
systems. In this study, four multilayer perceptron neural networks (MLPNNs) trained with different algorithms
were used for diabetes prediction and the most efficient training algorithm was determined. Backpropagation,
delta-bar-delta, extended delta-bar-delta and quick propagation were the studied four training algorithms. The
MLPNNs were trained, cross validated and tested with subject records from the database. Performance indicators
and statistical measures were used for evaluating the MLPNNs and the results demonstrated that the quick
propagation algorithm was the most efficient multilayer perceptron training algorithm for diabetes prediction. 

Anahtar Kelimeler

Multilayer perceptron neural network , training algorithms , diabetes diagnosis

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