Performance of small-world feedforward neural networks for the diagnosis of diabetes
Okan Erkaymaz, M Özer, Matjaz Perc
article (Uluslararası, Hakemli) | Yazar(lar): Okan Erkaymaz, M Özer, Matjaz Perc | Yıl: 2017 | Dergi/Kurum: APPLIED MATHEMATICS AND COMPUTATION | İndeks: WOS.SCI | DOI: 10.1016/j.amc.2017.05.010 | Anahtar Kelimeler: Diabetes, Small-world network, Feedforward neural network, Rewiring, Newman-Watts model, Watts-Strogatz
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. (C) 2017 Elsevier Inc. All rights reserved.