LDL kolesterolün yapay sinir ağları ile tahmini
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2013
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İnönü Üniversitesi
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info:eu-repo/semantics/openAccess
Abstract
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, üç farklı algoritma ile eğitilen çok katmanlı sinir ağları LDL Kolesterol?ün tahmininde kullanılmış ve en başarılı algoritma belirlenmiştir. Öğrenme oranı ve momentumlu geri yayılım algoritması, Levenberg-Marquardt geriyayılım algoritması ve Bayesyen düzeltmeye dayalı geri yayılım algoritması çalışılmış olan üç algoritmadır. Çok katmanlı sinir ağlarının eğitimi ve testi veri tabanında yer alan farklı kişilere ait kayıtlar ile yapılmıştır. Performans belirleyiciler ve istatistiksel ölçümler ile çok katmanlı sinir ağları değerlendirilmiş ve sonuçlar LDL Kolesterol?ün tahmininde Bayesyen düzeltmeye dayalı geri yayılım algoritmasının en başarılı eğitim algoritması olduğunu göstermiştir.
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, three multilayer neural networks trained with different algorithms were used for estimation of LDL Cholesterol and the most efficient training algorithm was determined. Gradient descent with momentum and adaptive learning rate backpropagation, Levenberg-Marquardt backpropagation, Bayesian regulation backpropagation were the studied three training algorithms. The multilayer neural networks were trained and tested with subject records from the database. Performance indicators and statistical measures were used for evaluating the multilayer neural networks and the results demonstrated that the Bayesian regulation backpropagation algorithm was the most efficient training algorithm for estimation of LDL Cholesterol.
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, three multilayer neural networks trained with different algorithms were used for estimation of LDL Cholesterol and the most efficient training algorithm was determined. Gradient descent with momentum and adaptive learning rate backpropagation, Levenberg-Marquardt backpropagation, Bayesian regulation backpropagation were the studied three training algorithms. The multilayer neural networks were trained and tested with subject records from the database. Performance indicators and statistical measures were used for evaluating the multilayer neural networks and the results demonstrated that the Bayesian regulation backpropagation algorithm was the most efficient training algorithm for estimation of LDL Cholesterol.
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Kızılkaya, O. (2013). LDL kolesterolün yapay sinir ağları ile tahmini. İnönü Üniversitesi Sosyal Bilimler Enstitüsü. 1-83 ss.
