Research of Feature Selection Methods to Predict Breast Cancer
K. Venkateswara Rao1, L. Mary Gladence2, V. Raja Lakshmi3
1K. Venkateswara Rao, Research Scholar, Sathyabama Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Dr. L. Mary Gladence, Associate Professor, Department of IT, Sathyabama Institute of Science and Technology, Chennai (Tamil Nadu), India.
3Dr. V. Raja Lakshmi, Associate Professor, Department of CSE, Sathyabama Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 15 October 2019 | Revised Manuscript received on 24 October 2019 | Manuscript Published on 02 November 2019 | PP: 2353-2355 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B12680982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1268.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Human health is most important than anything in the world, one should take care of it. Among various disease, cancer is the most terrible and deadly disease, so it is necessary to predict such disease in early stage. In this paper different feature selection methods used for feature extraction with different feature classification methods to identify the breast cancer. Breast cancer data is taken from UCI repository and is processed using WEKA tool and proposed techniques are applied to classify data accurately. This study well defines that data mining approach is suitable for predicting breast cancer.
Keywords: Cancer; Feature Selection; Classification; WEKA.
Scope of the Article: Probabilistic Models and Methods