Title: Genetic optimised serial hierarchical fuzzy classifier for breast cancer diagnosis

Authors: Xiao Zhang; Enrique Onieva; Asier Perallos; Eneko Osaba

Addresses: Hubei Provincial Engineering Research Center for Intelligent Management of Manufacturing Enterprises, College of Computer Science, South-Central University for Nationalities, Wuhan, China ' Deusto Institute of Technology (DeustoTech), University of Deusto, Bilbao, Spain ' Deusto Institute of Technology (DeustoTech), University of Deusto, Bilbao, Spain ' TECNALIA Research and Innovation, ETSI Bilbao UPV/EHU, Calle Luis Briñas, 5, 48013 Bilbao, Spain

Abstract: Accurate early-stage medical diagnosis of breast cancer can improve the survival rates and fuzzy rule-base system (FRBS) has been a promising classification system to detect breast cancer. However, the existing classification systems involves large number of input variables for training and produces a large number of fuzzy rules, which lead to high complexity and barely acceptable accuracy. In this paper, we present a genetic optimised serial hierarchical FRBS, which incorporates lateral tuning of membership functions and optimisation of the rule base. The serial hierarchical structure of FRBS allows selecting and ranking the input variables, which reduces the system complexity and distinguish the importance of attributes in datasets. We conduct an experimental study on Original Wisconsin Breast Cancer Database and Wisconsin Breast Cancer Diagnostic Database from UCI Machine Learning Repository, and show that the proposed system can classify breast cancer accurately and efficiently.

Keywords: genetic algorithm; fuzzy logic; classification system; breast cancer diagnosis; variable selection.

DOI: 10.1504/IJBIC.2020.107490

International Journal of Bio-Inspired Computation, 2020 Vol.15 No.3, pp.194 - 205

Accepted: 29 Dec 2019
Published online: 25 May 2020 *

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