A Review of Machine Learning Algorithms on Different Breast Cancer Datasets

Machine Learning (ML) algorithms have been used widely in the domain of medical science especially in classifying clinical data. Random Forest, Decision Tree, K-Nearest Neighbor, Support Vector Machine, Naive Bayes, Logistic Regression, and Multilayer Perceptron are some of the ML algorithms used for classification and prediction of various diseases. This paper reviews 40 recent ML algorithms published for breast cancer classification and breast cancer prediction along with the associated data pre-processing and feature selection techniques. The paper identifies from literature the pre-processing, feature selection steps, and the ML algorithms used for classification and prediction of breast cancer and tabulates them according to the accuracy. The paper also briefs the aspects of three common clinical breast cancer datasets used to train most such ML algorithms. The review helps prospective researchers in identifying different aspects of research in the domain of providing ML solutions from breast cancer datasets using suitable pre-processing and feature selection techniques.