Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes

Artificial intelligent systems have been widely used for diagnosis of diseases. Due to their importance, new approaches are attempted consistently to increase the performance of these systems. In this study, we introduce a new approach for diagnosis of diabetes based on the Small-World Feed Forward Artificial Neural Network (SW- FFANN). We construct the small-world network by following the Watts-Strogatz approach, and use this architecture for classifying the diabetes, and compare its performance with that of the regular or the conventional FFANN. We show that the classification performance of the SW-FFANN is better than that of the conventional FFANN. The SW-FFANN approach also results in both the highest output correlation and the best output error parameters. We also perform the accuracy analysis and show that SW-FFANN approach exhibits the highest classifier performance. © 2015 Elsevier Ltd. All rights reserved.

Eser Adı
[dc.title]
Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes
Yazar
[dc.contributor.author]
Erkaymaz, Okan
Yazar
[dc.contributor.author]
Özer, Mahmut
Yayın Yılı
[dc.date.issued]
2016
Yayıncı
[dc.publisher]
Elsevier Ltd
Yayın Türü
[dc.type]
article
Özet
[dc.description.abstract]
Artificial intelligent systems have been widely used for diagnosis of diseases. Due to their importance, new approaches are attempted consistently to increase the performance of these systems. In this study, we introduce a new approach for diagnosis of diabetes based on the Small-World Feed Forward Artificial Neural Network (SW- FFANN). We construct the small-world network by following the Watts-Strogatz approach, and use this architecture for classifying the diabetes, and compare its performance with that of the regular or the conventional FFANN. We show that the classification performance of the SW-FFANN is better than that of the conventional FFANN. The SW-FFANN approach also results in both the highest output correlation and the best output error parameters. We also perform the accuracy analysis and show that SW-FFANN approach exhibits the highest classifier performance. © 2015 Elsevier Ltd. All rights reserved.
Kayıt Giriş Tarihi
[dc.date.accessioned]
2019-12-23
Açık Erişim Tarihi
[dc.date.available]
2019-12-23
Yayın Dili
[dc.language.iso]
eng
Konu Başlıkları
[dc.subject]
Diabetic
Konu Başlıkları
[dc.subject]
Feed forward artificial neural network
Konu Başlıkları
[dc.subject]
Small-world network
Künye
[dc.identifier.citation]
Erkaymaz, O. ve Ozer, M. (2016). Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes. Chaos, Solitons & Fractals, 83, 178–185. doi:https://doi.org/10.1016/j.chaos.2015.11.029
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
ISSN
[dc.identifier.issn]
0960-0779
İlk Sayfa Sayısı
[dc.identifier.startpage]
178
Son Sayfa Sayısı
[dc.identifier.endpage]
185
Dergi Adı
[dc.relation.journal]
Chaos, Solitons and Fractals
Dergi Cilt Bilgisi
[dc.identifier.volume]
83
Tek Biçim Adres
[dc.identifier.uri]
https://dx.doi.org/10.1016/j.chaos.2015.11.029
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/6165
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247
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1
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22 Haziran 2024 06:39
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Tıklayınız
approach performance SW-FFANN conventional diabetes highest output Artificial systems diagnosis accuracy Elsevier classification rights regular reserved better perform results classifier correlation compare exhibits analysis parameters network classifying approaches increase consistently attempted importance diseases widely introduce
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