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Breast Cancer Prediction System: A novel approach to predict the accuracy using Majority-Voting Based Hybrid Classifier (MBHC)

Breast Cancer is the frequently recognized cancer growth among ladies and a significant explanation behind the expanded death rate among ladies. It takes more time to diagnose manually and very few automated prediction systems were present, leads to building up a new prediction framework that identifies the tumors in early-stage with more prediction accuracy. Machine Learning algorithms are using numerous classification algorithms were applied to classify whether the tumors are either benign or malignant in nature. Classification algorithms are well trained from the previous data and these can predict the new pattern from the current data received. Existing automated Breast Cancer prediction system gave lesser prediction accuracy from the Breast Cancer data set and the dataset is only about the image attributes which is not much convenient for the users. To enhance the accuracy of various prediction models, a novel Majority-Voting Based Hybrid Classifier (MBHC) is proposed to overcome the problems of predicting Breast Cancer. The experimental results of Breast Cancer Prediction System which use MBHC produced an accuracy score of 79%.