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Breast Cancer Wisconsin (Diagnostic) Data Analysis Using GFS

version 1.0.0 (546 KB) by Allison Murphy
Breast Cancer Wisconsin (Diagnostic) UCI Data analyzed using clustering and a Genetic Fuzzy Algorithm


Updated 16 Apr 2021

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Developing an accurate system to analyze breast cancer image data can give doctors an extra measure of confidence in assessing patients and can be used to scan all past scans in a clinic database to understand if any patients are at risk. Fuzzy Logic Systems are well suited to building knowledge bases and rule bases that can accurately approximate expert human knowledge, such as a doctor who routinely diagnoses breast cancer in patients. Genetic Algorithms boost the capability of a Fuzzy Logic System, especially when given a representative data set of the system, by using a subset of the data to learn the optimal membership functions and rule base of the Fuzzy Logic System. The combination of these two techniques can be utilized to develop a highly accurate approximator of cancer diagnosis.

Cite As

Allison Murphy (2021). Breast Cancer Wisconsin (Diagnostic) Data Analysis Using GFS (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2020b
Compatible with any release
Platform Compatibility
Windows macOS Linux
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