A Novel System Design for Prognosis on Breast Cancer using Machine Learning Algorithms
Soundari D.V1, Karthick. S2, Anish Fathima. B3, Dinesh Kumar. J. R.4, Priyadharsini. K5

1Soundari D.V*, AP/ECE, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India.
2Dr. S. Karthick, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andra Pradesh, India.
3Anish Fathima .B , AP, ECE, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India.
4Dinesh Kumar J.R, AP, ECE, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India.
5Priyadharsini K, AP, ECE, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India.
Manuscript received on January 12, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 1484-1490 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1622029420/2020©BEIESP | DOI: 10.35940/ijitee.D1622.029420
Open Access | Ethics and Policies | Cite | Mendeley
© 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: Cancer is the term used to describe a class of disease in which abnormal cells divide uncontrolledly and invade body tis sues. There are more than 100 unique types of cancer. Breast cancer is one of the women’s deadly disease. The prediction is done at the earlier stage and the results are accurate, the number of death per year can be reduced. So ultimately a new approach is needed to predict the level of cancer at the early stage which shows accurate results on prediction level. Hence Machine learning algorithms are used to predict the level of accuracy. Henceforth this paper analyze the different machine learning algorithm to predict the best levels of cancer and comparative statement was made about accuracy and the results showing SVM is more accurate. 
Keywords:  SVM, K-Means, Naive bayes, c4.
Scope of the Article:  Machine Learning