A Novel Approach for Detecting the Malignant Features of Breast Cancer using Algorithms of ML

Abstract

Machine learning (ML) is trending knowledge tool for finding disease. It facilitates systems that can learn data automatically. Breast cancer (BC) is second largest disease among the women. In all over world 50 % women are dying due to the BC. ML is used in finding the results in BC as malignant or benign because predication of BC at early stage is going to be very challenging. This proposed work is to identify the BC disease before time stage with the help of machine learning algorithms viz. K-Nearest Neighbor, Random Forest, Naive Bayes, Support Vector Machine (SVM) & Decision Tree. Herein projected work, the dataset used has been collect from the UCI repository. In Breast cancer dataset Out of total 450 samples, 150 samples are found either benign or malignant. The results obtained to achieve higher performance measures show that RF gives best outcome by smallest amount inaccuracy rate.