A Fuzzy-ACO Method for Detect Breast Cancer

Amin Einipour

Abstract


Data mining usually means the methodologies and tools for the efficient new knowledge discovery from databases. It is also a form of knowledge discovery essential for solving problems in a specific domain. For instance, the data mining approaches are applied in the filed of medical diagnosis recently. A major class of problems in medical science involves the diagnosis of disease, based upon various tests performed upon the patient. When several tests are involved, the ultimate diagnosis may be difficult to obtain, even for a medical expert. This has given rise, over the past few decades, to computerized diagnostic tools, intended to aid the physician in making sense out of the welter of data. Specifically, where breast cancer is concerned, the treating physician is interested in ascertaining whether the patient under examination exhibits the symptoms of a benign case, or whether her case is a malignant one. In this paper, we have focused on breast cancer diagnosis by combination of fuzzy systems and evolutionary algorithms. Fuzzy rules are desirable because of their interpretability by human experts. Ant colony algorithm is employed as evolutionary algorithm to optimize the obtained set of fuzzy rules. Results on breast cancer diagnosis data set from UCI machine learning repository show that the proposed approach would be capable of classifying cancer instances with high accuracy rate in addition to adequate interpretability of extracted rules.


Full Text: PDF DOI: 10.5539/gjhs.v3n2p195

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This work is licensed under a Creative Commons Attribution 3.0 License.

Global Journal of Health Science   ISSN 1916-9736(Print)   ISSN 1916-9744(Online)

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