Fuzzy-neural networks for medical diagnosis. (15th November 2010)

• Record Type: Journal Article
• Authors: Senol, Canan; Yildirim, Tulay
• Is Part Of: International journal of reasoning-based intelligent systems. Volume 2:Number 3/4(2010)
• Page Start: 265
• Page End: 271
• Publication Date: 2010-11-15
• Subjects: fuzzy CSFNN -- fuzzy MLP -- fuzzy RBF -- fuzzy-neural hybrid schemes -- medical diagnosis -- fuzzy neural networks -- conic section function neural networks -- multilayer perceptron -- radial basis function -- thyroid disorders -- breast cancer -- diabetes -- disease diagnosis -- simulation -- helthcare technology -- electronic healthcare -- e-healthcare; Artificial intelligence -- Periodicals; Reasoning -- Periodicals; 006.3
• Languages: English
• ISSNs: 1755-0556

Abstract

In this paper, a novel fuzzy-neural network architecture is proposed and the algorithm is developed. Using this new architecture, fuzzy-CSFNN, fuzzy-MLP and fuzzy-RBF configurations were constituted, and their performances have been compared on medical diagnosis problems. Here, conic section function neural network (CSFNN) is also a hybrid neural network structure that unifies the propagation rules of multilayer perceptron (MLP) and radial basis function (RBF) neural networks at a unique network by its distinctive propagation rules. That means CSFNNs accommodate MLPs and RBFs in its own self-network structure. The proposed hybrid fuzzy-neural networks were implemented in a well-known benchmark medical problems with real clinical data for thyroid disorders, breast cancer and diabetes disease diagnosis. Simulation results show that proposed hybrid structures outperform both MATLAB-ANFIS and non-hybrid structures.