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.
| Yazar |
Erkaymaz, Okan Özer, Mahmut |
| Yayın Türü | Article |
| Tek Biçim Adres | https://hdl.handle.net/20.500.12628/6165 |
| Tek Biçim Adres | 10.1016/j.chaos.2015.11.029 |
| Konu Başlıkları |
Diabetic
Feed forward artificial neural network Small-world network |
| Koleksiyonlar |
Fakülteler Mühendislik Fakültesi Bilgisayar Mühendisliği Bölümü Makale Koleksiyonu (Bilgisayar Mühendisliği Bölümü) Fakülteler Mühendislik Fakültesi Elektrik - Elektronik Mühendisliği Bölümü Makale Koleksiyonu (Elektrik - Elektronik Mühendisliği Bölümü) Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed | SOBİAD Scopus İndeksli Yayınlar Koleksiyonu Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed | SOBİAD WoS İndeksli Yayınlar Koleksiyonu |
| Dergi Adı | Chaos, Solitons and Fractals |
| Dergi Cilt Bilgisi | 83 |
| Sayfalar | 178 - 185 |
| Yayın Yılı | 2016 |
| 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 |
