Yapay Sinir Ağları ve Karar Ağaçları Teknikleri Kullanarak Migren Teşhisi

Yıl 2014, , 79 - 90, 01.01.2014

Ufuk Celık Nilufer Yurtay Ziynet Pamuk

https://doi.org/10.5824/1309-1581.2014.1.005.x

Öz

Geniş çaplı tıp alanında bilgisayar destekli çalışmalar son yıllarda büyük ölçüde artmıştır. Ayrıca, birçok tıbbi kuruluşlar farklı hastalıklar için veritabanları inşa etmeye devam etmektedir. Hastalığın belirlenmesi için yapay zeka tekniklerine hazırlanan bu tıp veritabanları paha biçilmez değerdedir. Bu çalışmada karar ağaçlarından Gini alogritması ve yapay sinir ağlarından dağıtılmış gecikme ağı, olasılık sinir ağı, ileri beslemeli ağ ve öğrenme vector nicelemesi migren ve olası migren teşhis amacıyla kullanılmıştır. Bu tekniklerin performansı karşılaştırılmış ve dağıtılmış gecikme ağ tekniği 95.45% doğruluk ile iyi tanı olarak görülmüştür.

Anahtar Kelimeler

Başağrısı, Migren, Karar Ağaçları, Yapay Sinir Ağları, Tıbbi Teşhis

Migraine Diagnosis by Using Artificial Neural Networks and Decision Tree Techniques

Öz

Computer supported studies in wide range of medical fields have been greatly expanded in recent years. Also, many medical organizations continue to build databases for different diseases. This medical database for artificial intelligence techniques for the determination of the disease is invaluable. As a subset, artificial neural networks and decision tree techniques are used for disease diagnosis. In this study Gini algorithm from decision trees and distributed delay network, probabilistic neural network, feed-forward network and learning vector quantization from artificial neural network have been used in order to diagnose migraine and probable migraine. Performance of these techniques has been compared and distributed delay network technique is observed as the best diagnosis with 95.45% accuracy.

Anahtar Kelimeler

headache, migraine, decision-tree, artificial neural networks, medical diagnosis

Kaynakça

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