Performance of small-world feedforward neural networks for the diagnosis of diabetes

We investigate the performance of two different small-world feedforward neural networks for the diagnosis of diabetes. We use the Pima Indians Diabetic Dataset as input. We have previously shown than the Watts–Strogatz small-world feedforward neural network delivers a better classification performance than conventional feedforward neural networks. Here, we compare this performance further with the one delivered by the Newman–Watts small-world feedforward neural network, and we show that the latter is better still. Moreover, we show that Newman–Watts small-world feedforward neural networks yield the highest output correlation as well as the best output error parameters. © 2017 Elsevier Inc.

Eser Adı
[dc.title]
Performance of small-world feedforward neural networks for the diagnosis of diabetes
Yazar
[dc.contributor.author]
Erkaymaz, Okan
Yazar
[dc.contributor.author]
Özer, Mahmut
Yazar
[dc.contributor.author]
Perc, Matjaž
Yayın Yılı
[dc.date.issued]
2017
Yayıncı
[dc.publisher]
Elsevier Inc.
Yayın Türü
[dc.type]
article
Özet
[dc.description.abstract]
We investigate the performance of two different small-world feedforward neural networks for the diagnosis of diabetes. We use the Pima Indians Diabetic Dataset as input. We have previously shown than the Watts–Strogatz small-world feedforward neural network delivers a better classification performance than conventional feedforward neural networks. Here, we compare this performance further with the one delivered by the Newman–Watts small-world feedforward neural network, and we show that the latter is better still. Moreover, we show that Newman–Watts small-world feedforward neural networks yield the highest output correlation as well as the best output error parameters. © 2017 Elsevier Inc.
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]
Diabetes
Konu Başlıkları
[dc.subject]
Feedforward neural network
Konu Başlıkları
[dc.subject]
Newman–Watts model
Konu Başlıkları
[dc.subject]
Rewiring
Konu Başlıkları
[dc.subject]
Small-world network
Konu Başlıkları
[dc.subject]
Watts–Strogatz model
Künye
[dc.identifier.citation]
Erkaymaz, O., Ozer, M. ve Perc, M. (2017). Performance of small-world feedforward neural networks for the diagnosis of diabetes. Applied Mathematics and Computation, 311, 22–28. doi:10.1016/j.amc.2017.05.010
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
ISSN
[dc.identifier.issn]
0096-3003
İlk Sayfa Sayısı
[dc.identifier.startpage]
22
Son Sayfa Sayısı
[dc.identifier.endpage]
28
Dergi Adı
[dc.relation.journal]
Applied Mathematics and Computation
Dergi Cilt Bilgisi
[dc.identifier.volume]
311
Tek Biçim Adres
[dc.identifier.uri]
https://dx.doi.org/10.1016/j.amc.2017.05.010
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/6970
Görüntülenme Sayısı ( Şehir )
Görüntülenme Sayısı ( Ülke )
Görüntülenme Sayısı ( Zaman Dağılımı )
Görüntülenme
28
09.12.2022 tarihinden bu yana
İndirme
1
09.12.2022 tarihinden bu yana
Son Erişim Tarihi
11 Nisan 2024 03:53
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Tıklayınız
feedforward neural small-world performance networks output network better Newman–Watts latter correlation Moreover highest parameters Elsevier delivered classification different diagnosis diabetes Indians Diabetic Dataset further previously Watts–Strogatz delivers investigate conventional compare
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