An Improved Model for Breast Cancer Classification Using Svm with Grid Search Method
Anita Paneri1, Mayank Patel2
1Anita Paneri, M. Tech Student, Department of CSE, Geetanjali Institute of Technical Studies, Udaipur, Rajasthan, India.
2Mayank Patel, M. Tech Student, Department of CSE, Geetanjali Institute of Technical Studies, Udaipur, Rajasthan, India.
Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 2731-2734 | Volume-8 Issue-8, June 2019 | Retrieval Number: H7332068819/19©BEIESP
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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: In today’s era, unhealthy lifestyle is the main cause of increase in diseases among human beings. Breast cancer (BC) is one such disease responsible for a sudden increase in death rate among women. Breast cancer mass is mainly classified into two Benign and Malignant. Benign refer to non-cancerous which means it cannot spread through other parts of the body while malignant is cancerous that is it can spread through other parts of the body. If it is detected on early stage, it can be treated on time. In this paper, we will use machine learning algorithms for breast cancer classification into B (benign) and M (malignant). Here, we will use Wisconsin Breast cancer Data Set and will apply Support Vector Machine (SVM) using python and then will develop an improved model using SVM-GSM (Grid Search Method) model for breast cancer classification and will analyze their results accordingly.
Keyword: Benign, Malignant, Support Vector Machine, Grid Search Method.
Scope of the Article: Classification.