Breast Cancer Prediction using Ensemble Voting Classifiers in Next Generation Sequences

Abstract Breast cancer has become the greatest frequent cancer among worldwide. Machine learning techniques contribute much tocancer prognosis. The prime focus of thework is to enhance the prognosis of breast cancer at an earlier stage using an ensemble of machine learning classifiers. Next generation genetic sequences of homo sapiens,BRCA1and BRCA2from National Centre for Biotechnology Information were derived for prediction of breast cancer. The proposed ensembled classifiers by hard voting and soft voting,combinedmodelslike Decision Tree technique, SVMalgorithm, LR statistical model, Linear Discriminant analysis model, Naive Bayes classifier and k-nearest neighbours’ algorithm.Five ensembled models from 6 machine learning classifiers were concatenated for the prediction purpose. Classification accuracy of ensemble hard voting and soft voting classifiers were evaluated statistically.Soft voting classifier for model 1(DT & SVM) and model2(DT, SVM&LR) achieved greatest value for classification performance metrics. Among all ensembled models, model 1 as well as model 2 achieved maximum classification precision of 94%.