Pattern Recognition in Breast Cancer Using Machine Learning

Breast Cancer is the most often identified cancer among women and major reason for increasing mortality rate women. As diagnosis of this disease manually takes long hours lesser availability systems, there a need to develop automatic system early detection cancer. Data mining techniques contribute lot in development such system. For classification benign malignant tumor we have used machine learning which learned from past data can predict category new input. This paper relative study on implementation models using Logistic Regression, Support Vector Machine (SVM) Latent Dirichlet Allocation (LDA) done dataset taken UCI repository. With respect results accuracy, precision, sensitivity, specificity False Positive Rate efficiency each algorithm measured compared. leading malignancy affecting 2.1 million year leads greatest number deaths Early treatment not only helps cure but also prevention its recurrence. And hence mainly focuses prediction breast where it uses different algorithms creating like decision tree, logistic regression, random forest are applied pre-processed suspects greater accuracy prediction. Amongst all models, Random Forest Classification best with 98.6%