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

Okan Erkaymaz, Mahmut Ozer and Matjaž Perc

Applied Mathematics and Computation, 2017, vol. 311, issue C, 22-28

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.

Keywords: Diabetes; Small-world network; Feedforward neural network; Rewiring; Newman–Watts model; Watts–Strogatz model (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:311:y:2017:i:c:p:22-28

DOI: 10.1016/j.amc.2017.05.010

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