Investigation of Mihp Codes over Free Space Optical Communication

Abstract

Breast cancer has become the greatest frequent cancer among worldwide. Machine learning techniques contribute much to cancer prognosis. The prime focus of the work 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, BRCA1 and BRCA2 from National Centre for Biotechnology Information were derived for prediction of breast cancer. The proposed ensembled classifiers by hard voting and soft voting, combined models like Decision Tree technique, SVM algorithm, 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 model 2(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%.