Augmentation of Classifier Accuracy through Implication of Feature Selection for Breast Cancer Prediction
Deepa B G1, Senthil S2, Gupta Rahil M3, Shah Vishakha R4 

1Mrs. Deepa, B G, Assistant Professor, holds MCA in Computer Applications from VTU and B.Sc. in Computer Science from Kuvempu University.
2Dr. S. Senthil, Professor and Director, School of Computer Science and Applications, REVA University, India.
3Gupta Rahil M, School of Computer Science and Applications, REVA University, India.
4Shah3Vishakha R, School of Computer Science and Applications, REVA University, India.

Manuscript received on 01 March 2019 | Revised Manuscript received on 08 March 2019 | Manuscript published on 30 July 2019 | PP: 6396-6399 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2216078219/19©BEIESP | DOI: 10.35940/ijrte.B2216.078219
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Abstract: Breast Cancer Examination and Prediction are great provocations to the researchers in the medical applications. Breast Cancer Examination distinguishes benign from malignant breast lumps, Breast Cancer Prediction has great deal in foretelling when Breast Cancer is expected to reoccur in patients that have had their cancers excised. Feature Selection is considered to be the preliminary step used in process to find best subsets of attributes. In this paper authors confer about the performance of five classifiers Sequential minimal optimization (SMO), Multilayer Perceptrons, Kstar, Decision Table and Random Forest with and without feature selection. The results manifest that after implying two feature selection techniques such as Correlation based and information based with ranker algorithm there is an augmentation in the accuracy rate of the classifier. It has been observed that after through implication feature selection techniques accuracy of the classifiers such as SMO, Multilayer Perceptrons, Kstar, Decision Trees, and Random Forest are enhanced.
Index Terms: Breast Cancer, Feature Selection, Data Mining. SMO, Multilayer Perceptron’s, Kstar, Decision Table, Random Forest, Information Gain Based Feature Selection, Correlation Based Feature Selection.

Scope of the Article: Data Mining