Multilevel Classification Algorithm using Diagnosis and Prognosis of Breast Cancer

Abstract

In order to analyse the chosen data from various points of view, data mining is used as the effective process. This process is also used to sum up all those views 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 used to bifurcate the affected image from the given affected image and foretell one or more discrete variables, based on the other attributes in the dataset. The ID3 (Iterative Dichotomiser 3) algorithm is an original affected image S as the root node. An unutilised attribute of the affected image 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 a heuristic quest that imitates the process of natural selection. Genetic algorithm can easily select cancer affected image 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 and decision tree algorithm easily identifies breast cancer affected image. The genetic based classification algorithm diagnosis and prognosis of breast cancer affected is identified by this paper.