ONURSAL ÇETİN, FEYZULLAH TEMURTAŞ, Şenol GÜLGÖNÜL

An application of multilayer neural network on hepatitis disease diagnosis using approximations of sigmoid activation function

Amaç: Hepatit hastalığının teşhisi için çok katmanlı sinir ağı (MLNN) ve sigmoid aktivasyon fonksiyonu uygulanmıştır. Yöntemler: Yapay sinir ağları (YSA) tıbbi tanı için halen yaygın olarak kullanılan etkili araçlardır. Donanım tabanlı mimarilerde aktivasyon fonksiyonları YSA davranışında önemli rol oynamaktadır. Sigmoid fonksiyonu yumuşak tepkisi nedeniyle en sık kullanılan aktivasyon fonksiyonudur. Bu nedenle, sigmoid fonksiyonu ve yaklaşımları aktivasyon fonksiyonu olarak uygulanmıştır. Veri kümesi UCI makine öğrenme veri tabanından alınmıştır. Bulgular: Hepatit hastalığının tanısı için, MLNN yapısı hayata geçirilmiş ve Levenberg Morquardt (LM) algoritması öğrenme için kullanılmıştır. Hepatit hastalığını sınıflandıran yöntemimiz 10-kat çapraz doğrulama yoluyla 91.9%den 93.8%e doğruluklar sağlamıştır. Sonuç: Yapay sinir ağları ve aynı veri setini kullanarak hepatit hastalığını teşhis eden önceki çalışma ile karşılaştırıldığında, bizim sonuçlarımız sinir ağı tabanlı donanımın boyutunu ve maliyetini azaltması bakımından umut vericidir. Böylece, donanım tabanlı tanı sistemleri sigmoid fonksiyonu yaklaşımları kullanılarak etkili bir şekilde geliştirilebilir.

Sigmoid aktivasyon fonksiyonu kestirimi kullanılarak karaciğer hastalığı tanısında çok katmanlı sinir ağı uygulaması

Objective: Implementation of multilayer neural network (MLNN) with sigmoid activation function for the diagnosis of hepatitis disease. Methods: Artificial neural networks (ANNs) are efficient tools currently in common use for medical diagnosis. In hardware based architectures activation functions play an important role in ANN behavior. Sigmoid function is the most frequently used activation function because of its smooth response. Thus, sigmoid function and its close approximations were implemented as activation function. The dataset is taken from the UCI machine learning database. Results: For the diagnosis of hepatitis disease, MLNN structure was implemented and Levenberg Morquardt (LM) algorithm was used for learning. Our method of classifying hepatitis disease produced an accuracy of 91.9% to 93.8% via 10 fold cross validation. Conclusion: When compared to previous work that diagnosed hepatitis disease using artificial neural networks and the identical data set, our results are promising in order to reduce the size and cost of neural network based hardware. Thus, hardware based diagnosis systems can be developed effectively by using approximations of sigmoid function.

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