Application and comparison of different classification methods basedon symptom analysis with traditional classification technique for breastcancer diagnosis

Abstract

Novel approach for classification technique such as Artificial Neural Network (ANN), Linear Discriminant
Analysis (LDA) and Random Forest (RF) using factor or dichotomic variables has been introduced. This
study searches for the highly informative finitely linear combinations (symptoms) of variables in the finite
field on the based of the Fisher’s exact test and accurately predict the target class for each case in the data.
There are several super symptoms have comparable ???? − ????????????????????????. In this case, it becomes possible to choose
as a nominative representative the factor which is more accessible for interpretation. The super symptom
means a linear combination of various multiplications of k dichotomous variables over a field of
characteristic 2 without repeating. In algebra, such functions are called Zhegalkin polynomials or algebraic
normal forms.
This process essentially yields the new variable of the identical nature or factor. The purpose of this study
is to suggest a classifiers in accordance with symptoms analysis, and accurately predict the class in the
dataset by using different algorithms. The proposed method for super symptoms with the most famous
classification methods was compared to traditional classification methods. The performance of the
classifiers has investigated based on breast cancer data set for training algorithm. Moreover, these three
different algorithms have been studied very well based on symptom analysis and thus we do focus on the
fact that the best results are from which algorithm.