Genetic optimised serial hierarchical fuzzy classifier for breast cancer diagnosis

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URI: https://hdl.handle.net/11556/3389
ISSN: 1758-0366
DOI: 10.1504/IJBIC.2020.107490
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2020
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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.
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Publisher Copyright: © 2020 Inderscience Enterprises Ltd.
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Breast cancer diagnosis , Classification system , Fuzzy logic , Genetic algorithm , Variable selection , Theoretical Computer Science , General Computer Science , SDG 3 - Good Health and Well-being
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journal article
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Zhang , X , Onieva , E , Perallos , A & Osaba , E 2020 , ' Genetic optimised serial hierarchical fuzzy classifier for breast cancer diagnosis ' , International Journal of Bio-Inspired Computation , vol. 15 , no. 3 , pp. 194-205 . https://doi.org/10.1504/IJBIC.2020.107490