Accuracy Enhancement for Breast Cancer Detection Using Classification and Feature Selection

Accuracy Enhancement for Breast Cancer Detection Using Classification and Feature Selection

Somil Jain, Puneet Kumar
Copyright: © 2022 |Pages: 15
DOI: 10.4018/IJIRR.299931
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Abstract

Chronic disease like kidney failure, heart disease, cancer etc. is the major cause of deaths now days worldwide. Especially for the females the most dangerous type of disease from which the women of every age group are suffering especially the middle age group women’s is the breast cancer. To detect this type of disease at an early stage is a challenging task. In order to predict the breast cancer at an early stage classification algorithm of high accuracy and less error rate are desirable. In this research work we have used 4 classification algorithms K-NN, J48, Logistic regression and Bayes Net for building the predictive model, also the wrapper method of feature selection is used to enhance the accuracy rate and reduce the error rate of the used classifiers. To carry out this research we have used Wisconsin Diagnostic Breast Cancer dataset which contains 569 instances along with 32 attributes and a class attribute which will predict the type of cancer i.e. Benign or Malignant.
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(Ii) Unsupervised Learning

Unsupervised learning is used to draw inferences from datasets that consist of unlabeled input data. The most popular unsupervised form of learning is cluster analysis, which is used to identify hidden patterns or to group in data for exploratory data analysis.

This paper is structured in the following manner:

  • i)

    Section 1 provides the general introduction and overview of the paper.

  • ii)

    Section 2 provides Literature review.

  • iii)

    Section 3 provides the methodology of the work to be done along with a brief introduction of the used methods.

  • iv)

    Section 4 this section shows the experimental results.

  • v)

    Section 5 provides the conclusion of the study.

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