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

作者：

Highlights：

• Small-world feedforward neural networks for the diagnosis of diabetes are considered.

• The Newman–Watts small-world model outperforms the Watts–Strogatz model.

• The Newman–Watts small-world model yields the highest output correlation.

• The Newman–Watts small-world model yields the best output error parameters.

摘要

•Small-world feedforward neural networks for the diagnosis of diabetes are considered.•The Newman–Watts small-world model outperforms the Watts–Strogatz model.•The Newman–Watts small-world model yields the highest output correlation.•The Newman–Watts small-world model yields the best output error parameters.

论文关键词：Diabetes,Small-world network,Feedforward neural network,Rewiring,Newman–Watts model,Watts–Strogatz model

论文评审过程：Received 9 March 2017, Revised 18 April 2017, Accepted 1 May 2017, Available online 15 May 2017, Version of Record 15 May 2017.

论文官网地址：https://doi.org/10.1016/j.amc.2017.05.010