SavedAdd your journal

Journals / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi / 2006 / Cilt: 21 - Sayı: 2

DIABETES DIAGNOSIS BY MULTILAYER PERCEPTRON NEURAL NETWORKS

ÇOK KATMANLI PERSEPTRON SİNİR AĞLARI İLE DİYABET HASTALIĞININ TEŞHİSİ

İnan GÜLER
Elif ÜBEYLİ
Pages
319–326
Publication Date
2013-03-01
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
📊
Key Findings & Data:Different ANN structures are valuable models, which are used in the medical field for the development of decision support systems.
💡
Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Çok katmanlı perseptron sinir ağı · eğitim algoritmaları · diyabet hastalığının teşhisi.

Abstract

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.

Özet

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 enbaşarılı çok katmanlı perseptron eğitim algoritması olduğunu göstermiştir.

🔖

Cite This Article

GÜLER, İ., ÜBEYLİ, E. (2013). DIABETES DIAGNOSIS BY MULTILAYER PERCEPTRON NEURAL NETWORKS. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, Cilt: 21 - Sayı: 2, 319–326. https://ajindex.com/articles/21134

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (15)

  1. Tafeit, E., Reibnegger, G., “Artificial Neural Networks in Laboratory Medicine and Medical Outcome Prediction”, Clinical Chemistry and Laboratory Medicine, Vol 37, No 9, 845-853, 1999.
  2. Güler, İ., Übeyli, E.D., “Detection of Ophthalmic Artery Stenosis by Least-Mean Squares Backpropagation Neural Network”, Computers in Biology and Medicine, Vol 33, No 4, 333-343, 2003.
  3. Übeyli, E.D., Güler, İ., “Neural Network Analysis of Internal Carotid Arterial Doppler Signals: Predictions of Stenosis and Occlusion”, Expert Systems with Applications, Vol 25, No 1, 1-13, 2003. Güler, İ, Übeyli, E.D., “ECG Beat Classifier Designed by Combined Neural Network Model”, Pattern Recognition, Vol 38, No 2, 199-208, 2005.
  4. Haykin, S., Neural Networks: A Comprehensive Foundation, New York, Macmillan College Publishing Company Inc, 1994.
  5. Chaudhuri, B.B., Bhattacharya, U., “Efficient Training and Improved Performance of Multilayer Perceptron in Pattern Classification”, Neurocomputing, Vol 34, 11-27, 2000.
  6. Rumelhart, D.E., Hinton, G.E., Williams, R.J., “Learning Representations by Back-Propagating Errors”, Nature, Vol 323, 533-536, 1986.
  7. Jacobs, R.A., “Increased Rate of Convergence through Learning Rate Adaptation”, Neural Networks, Vol 1, 295-307, 1988.
  8. Minai, A.A., Williams, R.D., “Back-Propagation Heuristics: A Study of the Extended Delta-Bar- Delta Algorithm”, Proceedings of International Joint Conference on Neural Networks, Vol 1, -600, San Diego, California, 17-21 June, 1990.
  9. Fahlman, S.E., “An Empirical Study of Learning Speed in Backpropagation Networks”, Computer Science Technical Report, CMU-CS-88-162,
  10. Carnegie Mellon University, Pittsburgh, 1988.
  11. Chan, L-W., “Efficacy of Different Learning Algorithms of the Back Propagation Network”, IEEE Region 10 Conference on Computer and Communication Systems, Vol 1, 23-27, 24-27 September, 1990.
  12. Sidani, A., Sidani, T., “A Comprehensive Study of the Back Propagation Algorithm and Modifications”, IEEE Conference Record, 80-, 29-31 March, 1994.
  13. Hannan, J.M., Bishop, J.M., “A Comparison of Fast Training Algorithms Over Two Real Problems”, IEE Fifth International Conference on Artificial Neural Networks, Conference Publication No. 440, 1-6, 7-9 July, 1997. Download Diabetes Data; 05.05.2005. http://www.cormactech.com/neunet/download.html,
  14. Besser, G.M., Bodansky, H.J., Cudworth A.G., Clinical Diabetes an Illustrated Text, Gower Medical Publishing, London, 1988.
  15. Kennedy, L., Lyons, T.J., “Non-Enzymatic Glycosylation”, British Medical Bulletin,