Breast Cancer Prediction using Machine Learning

Abstract

The primary identification and prediction of type of the cancer about to develop a compulsion in cancer study, in order to assist and supervise the patients. Further accurate classification of benign tumors can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research Logistic Regression, K-NN, SVM, Random Forest, Decision Tree has been proposed to predict the breast cancer. To produce deep predictions in a new environment on the breast cancer data. Besides this, this study predicts the best Model yielding high performance by evaluating dataset on various classifiers. In this paper Breast cancer dataset is collected from the UCI machine learning repository has 569 instances with 31 attributes. Data set is pre-processed first and fed to various classifiers like Logistic Regression, K-NN, SVM, Random Forest, Decision Tree. The algorithm with the best results will be used as the backend to the website and the model will then classify the cancer as benign or malignant.