Prognosis on Stratification of Breast Cancer using Data Mining Models
Sreelakshmi S Pai1, Ann Mary Simon2, G S Anisha3
1Sreelakshmi S Pai*, PG Student, Department of Computer Science &IT Amrita School of Arts and Sciences, Kochi Amrita Vishwa Vidyapeetham, India.
2Ann Mary Simon, PG Student, Department of Computer Science &IT Amrita School of Arts and Sciences, Kochi Amrita Vishwa Vidyapeetham, India.
3G S Anisha, Assistant Professor, Department of Computer Science &IT Amrita School of Arts and Sciences, Kochi Amrita Vishwa Vidyapeetham, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on April 10, 2020. | PP: 650-653 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3406049620/2020©BEIESP | DOI: 10.35940/ijitee.F3406.049620
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: Breast cancer classification can be useful for discovering the genetic behavior of tumors and envision the outcome of some diseases. Through this paper we are predicting the noxious behavior of a tumor. The prediction models used are Random Forest, Naïve Bayes, IBK (Instance Based Learner), SMO (Sequential minimal optimization), and Multi Class Classifier. This prediction model which can potentially be used as a biomarker of breast cancer is based on physical attributes of a breast mass and which is gathered from digitized image of Fine Needle Aspirate (FNA). These can be helpful in prediction and reduction of invasive tumors.
Keywords: Breast Cancer, Benign, Data Mining, Malignant
Scope of the Article: Data mining and warehousing