Emad Nabil

Citation:

Nabil, E., A. Badr, I. Farag, and O. Khozaiem, "A Hybrid Artificial Immune Genetic Algorithm with Fuzzy Rules for Breast Cancer Diagnosis ", 6th international Conference in informatics and systems, ,, Cairo, Egypt, 27-29 March, 2008.

Date Presented:

Abstract:

The automatic diagnosis of breast cancer is an important, real-world medical problem. In this paper we
give an introduction to fuzzy systems, genetic algorithms and artificial immune system, and then we introduce a
hybrid algorithm that gathers the genetic algorithms with the artificial immune system in one algorithm. The genetic
algorithm, the artificial immune system and the hybrid algorithm were implemented and tested on the Wisconsin
breast cancer diagnosis (WBCD) problem in order to generate a fuzzy rule system for breast cancer diagnosis.
The hybrid algorithm generated a fuzzy system which reached the maximum classification ratio earlier than the
two other ones. The motivations of using fuzzy rules incorporate with evolutionary algorithms in the underline
problem are attaining high classification performance with the possibility of attributing a confidence measure
(degree of benignity or malignancy) to the output diagnosis beside the simplicity of the diagnosis system
which means that the system is human interpretable.