An Analysis on Breast Disease Prediction Using Machine Learning Approaches

Abstract

The central aspect of this study is to evaluate the different Machine learning classifier's performance for the prediction of breast cancer disease. In this work, we have used six supervised classification techniques for the classification of breast cancer disease. For example, SVM, NB, KNN, RF, DT, and LR used for the early prediction of breast cancer. Therefore, we evaluated breast cancer dataset through sensitivity, specificity, f1 measure, and total accuracy. The prediction performance of breast cancer analysis shows that SVM obtained the uppermost performance with the utmost classification accuracy of 97.07%. Whereas, NB and RF have achieved the second highest accuracy by prediction. Our findings can help to reduce the existence of breast cancer disease through developing a machine learning-based predictive system for early prediction.