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Journals / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi / 2026 / Cilt: 21 Sayı: 2

DIABETES DIAGNOSIS BY MULTILAYER PERCEPTRON NEURAL NETWORKS

ÇOK KATMANLI PERSEPTRON SİNİR AĞLARI İLE DİYABET HASTALIĞININ TEŞHİSİ

İnan Güler
Elif Übeyli
Pages
319–326
Publication Date
2013-03-01
DOI
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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 en başarılı çok katmanlı perseptron eğitim algoritması olduğunu göstermiştir.

🔖

Cite This Article

Güler, İ., Übeyli, 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/1078985

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References (70)

  1. Tafeit, E., Reibnegger, G., “Artificial Neural
  2. Networks in Laboratory Medicine and Medical
  3. Outcome Prediction”, Clinical Chemistry and
  4. Laboratory Medicine, Vol 37, No 9, 845-853, 1999.
  5. Güler, İ., Übeyli, E.D., “Detection of Ophthalmic
  6. Artery Stenosis by Least-Mean Squares
  7. Backpropagation Neural Network”, Computers in
  8. Biology and Medicine, Vol 33, No 4, 333-343, 2003.
  9. Übeyli, E.D., Güler, İ., “Neural Network Analysis
  10. of Internal Carotid Arterial Doppler Signals:
  11. Predictions of Stenosis and Occlusion”, Expert
  12. Systems with Applications, Vol 25, No 1, 1-13, 2003.
  13. Güler, İ, Übeyli, E.D., “ECG Beat Classifier
  14. Designed by Combined Neural Network Model”,
  15. Pattern Recognition, Vol 38, No 2, 199-208, 2005.
  16. Haykin, S., Neural Networks: A Comprehensive
  17. Foundation, New York, Macmillan College
  18. Publishing Company Inc, 1994.
  19. Chaudhuri, B.B., Bhattacharya, U., “Efficient
  20. Training and Improved Performance of
  21. Multilayer Perceptron in Pattern Classification”,
  22. Neurocomputing, Vol 34, 11-27, 2000.
  23. Rumelhart, D.E., Hinton, G.E., Williams, R.J.,
  24. “Learning Representations by Back-Propagating
  25. Errors”, Nature, Vol 323, 533-536, 1986.
  26. Jacobs, R.A., “Increased Rate of Convergence
  27. through Learning Rate Adaptation”, Neural
  28. Networks, Vol 1, 295-307, 1988.
  29. Minai, A.A., Williams, R.D., “Back-Propagation
  30. Heuristics: A Study of the Extended Delta-Bar-
  31. Delta Algorithm”, Proceedings of International
  32. Joint Conference on Neural Networks, Vol 1,
  33. 600, San Diego, California, 17-21 June, 1990.
  34. Fahlman, S.E., “An Empirical Study of Learning
  35. Speed in Backpropagation Networks”, Computer
  36. Science Technical Report, CMU-CS-88-162,
  37. Carnegie Mellon University, Pittsburgh, 1988.
  38. Chan, L-W., “Efficacy of Different Learning
  39. Algorithms of the Back Propagation Network”,
  40. IEEE Region 10 Conference on Computer and
  41. Communication Systems, Vol 1, 23-27, 24-27
  42. September, 1990.
  43. Sidani, A., Sidani, T., “A Comprehensive Study
  44. of the Back Propagation Algorithm and
  45. Modifications”, IEEE Conference Record, 80-, 29-31 March, 1994.
  46. Hannan, J.M., Bishop, J.M., “A Comparison of
  47. Fast Training Algorithms Over Two Real
  48. Problems”, IEE Fifth International Conference
  49. on Artificial Neural Networks, Conference
  50. Publication No. 440, 1-6, 7-9 July, 1997.
  51. Download Diabetes Data; 05.05.2005.
  52. http://www.cormactech.com/neunet/download.html,
  53. Besser, G.M., Bodansky, H.J., Cudworth A.G.,
  54. Clinical Diabetes an Illustrated Text, Gower
  55. Medical Publishing, London, 1988.
  56. Kennedy, L., Lyons, T.J., “Non-Enzymatic
  57. Glycosylation”, British Medical Bulletin, Vol, No 1, 174-190, 1989.
  58. Bray, G.A., Contemporary Diagnosis and
  59. Management of Obesity, Newtown, Pennsylvania,
  60. Handsbook in Health Care Company, 2003.
  61. Hyöty, H., Hiltunen, M., Lönnrot, M., “Enterovirus
  62. Infections and Insulin Dependent Diabetes
  63. Mellitus-Evidence for Causality”, Clinical and
  64. Diagnostic Virology, Vol 9, No 2-3, 77-84, 1998.
  65. Pi-Sunyer, F.X., “A Review of Long-Term
  66. Studies Evaluating the Efficacy of Weight Loss in
  67. Ameliorating Disorders Associated with Obesity”,
  68. Clinical Therapeutics, Vol 18, No 6, 1006-1035,
  69. American Diabetes Association (ADA), 05.05.2005
  70. http://www.diabetes.org/home.jsp