Open AccessPrognosis on Stratification of Breast Cancer using Data Mining Models
Author(s) -
Sachin Pai,
Ann M. Simon,
G. S. Anisha
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.f3406.049620
Subject(s) - random forest , breast cancer , naive bayes classifier , classifier (uml) , artificial intelligence , computer science , machine learning , bayes' theorem , pattern recognition (psychology) , oncology , medicine , data mining , cancer , bayesian probability , support vector machine
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