Diagnosis and Prognosis of Breast Cancer Using Multi Classification Algorithm

Abstract

Data mining is the process of analysing data from different views points and condensing it into useful information. There are several types of algorithms in data mining such as Classification algorithms, Regression,Segmentation algorithms, Association algorithms, Sequence analysis algorithms, etc.,. The classification algorithm can be usedto bifurcate the data set from the given data set and foretell one or more discrete variables, based on the other attributes in the dataset. The ID3 (Iterative Dichotomiser 3) algorithm is an original data set S as the root node. An unutilised attribute of the data set S calculates the entropy H(S) (or Information gain IG (A)) of the attribute. Upon its selection, the attribute should have the smallest entropy (or largest information gain) value. A genetic algorithm (GA) is aheuristic quest that imitates the process of natural selection. Genetic algorithm can easily select cancer data set, from the given data set using GA operators, such as mutation, selection, and crossover. A method existed earlier (KNN+GA) was not successful for breast cancer and primary tumor. Our method of creating new algorithm GA+ID3 easily identifies breast cancer data set from the given data set. The multi classification algorithm diagnosis and prognosis of breast cancer data set is identified by this paper.