Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/5670
Title: Çok katmanlı perseptron sinir ağları ile diyabet hastalığının teşhisi
Other Titles: Diabetes diagnosis by multilayer perceptron neural networks
Authors: Güler, İnan
Übeyli, Elif Derya
Keywords: Diabetes diagnosis
Multilayer perceptron neural network
Training algorithms
Issue Date: 2006
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.
URI: https://search.trdizin.gov.tr/yayin/detay/57605
https://hdl.handle.net/20.500.11851/5670
ISSN: 1300-1884
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
TR Dizin İndeksli Yayınlar / TR Dizin Indexed Publications Collection

Show full item record

CORE Recommender

Page view(s)

24
checked on Dec 26, 2022

Google ScholarTM

Check


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.