Voting Classifier on Ensemble Algorithms for Breast Cancer Prediction

Abstract

One of the leading causes of death in humans is cancer, with Breast cancer accounting for the majority of deaths among women. Both men and women are developing more Breast cancer cases every day. Breast cancer has become a huge health concerning issue. If Breast cancer is detected in early stages like stages 0 and 1, it can be cured without too much health risk which will reduce the death rate. Computer-Aided Detection or CAD systems are available to detect whether a woman or man has Breast cancer or not. The purpose of our research paper is to find a technique or algorithm that can help in the early detection of Breast cancer to save lives and reduce casualties. Here, different Machine Learning (ML) algorithms like decision tree, Support vector machine, ensemble algorithm of random forest with bagging and Ada boosting, and Voting Classifier are used. Breast cancer prediction with a Voting Classifier on ensemble algorithms is built. The performance of applied models is analyzed in terms of Accuracy, Precision, Recall, F1-score, and Support. The Voting Classifier gives the best results with 0.96 Accuracy because it uses the mean of ensemble algorithms to improve Accuracy value.