Machine learning models applied in analyzing breast cancer classification accuracy

Abstract

There have been many attempts made to classify breast cancer data, since this classification is critical in a wide variety of applications related to the detection of anomalies, failures, and risks. In this study machine learning (ML) models are reviewed and compared. This paper presents the classification of breast cancer data using various ML models. The effectiveness of models comparatively evaluated through result using benchmark of accuracy which was not done earlier. The models considered for the study are k-nearest neighbor (kNN), decision tree classifier, support vector machine (SVM), random forest (RF), SVM kernels, logistic regression, Naïve Bayes. These classifiers were tested, analyzed and compared with each other. The classifier, decision tree, gets the highest accuracy i.e. 97.08% among all these models is termed as the best ML algorithm for the breast cancer data set.