A genetic algorithm based nearest neighbor classification to breast cancer diagnosis.

This paper presents an application of a hybrid approach (the genetic algorithms and the k-nearest neighbour) proposed by Ishbuchi to Wisconsin breast cancer data. For the diagnosis of breast cancer, the determination of the presence of benign/malignant breast tumors represents a very complex problem (even for an experienced cytologist). Therefore the automatic classification of benign and malignant symptoms is highly desirable as a valuable aid to assist oncologists in the decision making of the diagnosis of breast cancer. In this paper, the genetic algorithm based k-nearest neighbour method for classification of benign and malignant breast tumors is presented. The genetic-algorithm (GA) is used for finding a compact reference set by selecting a small number of reference patterns from a large number of training patterns in nearest neighbor classification. The GA simultaneously performs feature selection and pattern selection and prunes unnecessary features. The goal is to maximize the classification performance of the reference set and minimize the number of selected patterns and features. Results are also compared with a fuzzy-genetic approach where each reference patten represents a fuzzy if-then rule with a circular-cone-type membership function.