Impact of Dimensionality Reduction and Classification in Breast Cancer
Durgalakshmi B1, Vijayakumar V2

1Vijayakumar V, currently Associate Dean of the School of Computing Science and Engineering at VIT University, (Tamil Nadu), India.
2B. Durgalakshmi, currently pursuing PhD in the School of Computing Science and Engineering at VIT University,chennai, (Tamil Nadu), India.

Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 2599-2603 | Volume-8 Issue-8, June 2019 | Retrieval Number: H7448068819/19©BEIESP
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Abstract: Breast cancer is the main reason for the female casualty across the world and researchers are aiming to provide a best solution to early diagnosis so that the mortality rate can be reduced. Inorder to understand the problem, the Wisconsin prognostic Breast cancer (WPBC) data set has been obtained from the UCI is utilized for medical research by selecting the best features by correlation matrix. Dimensionality reduction and memory optimization is achieved by suing the feature selection algorithm. Followed with, the classifiers such as support vector classification, logistic regression and random forest is deployed to provide high detection accuracy and reduced error rate. The classifier model thus compares the accuracy with the existing methods and the best classifier model is built. Thus, the efficient model is subjected to the breast cancer cells detection and the improved results provides a major contribution to the early diagnosis of the cancer cells.
Keyword: Breast cancer, Principal Compound Analysis,WDBC dataset.
Scope of the Article: Classification.