Breast cancer detection and classification approach based on ensemble learning
International Journal of Advanced Trends in Computer Science and Engineering · 2020
The application of machine learning is constantly increasing especially in the field of automated disease diagnose and prediction such as breast cancer, which is very common in females as it can cause too many death but machine learning can increase the chances of survival by early prognosis and diagnosis if it diagnosed properly and accurately.In this paper we propose an automated breast cancer prediction approach based on the XgBoost random forest classifier(XGBRF) algorithm.In order to prove the effectiveness and accurateness of the proposed approach, Wisconsin diagnose breast cancer dataset is usedover which various classification rates like precision, recall , F1score and confusion matrix are generated.The testing accuracy of our proposed approach is 99%.Apart from that the proposed approach is being compared with various other approaches based on other machine learning classifiers like support vector machine, K-nearest neighbor, Naïve Bayes etc. .