Abstract—Breast cancer is a form of invasive cancer and one of the most common health problems for women that is globally responsible for a large number of deaths. Accurately classifying and categorizing breast cancer subtype is an essential task. Automated techniques based on artificial intelligence can significantly save time and reduce error. In this paper, a performance comparison between five nonlinear machine learning algorithms viz Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Gaussian Nave Bayes (NB) and Support Vector Machines (SVM) on the Wisconsin Breast Cancer Diagnostic (WBCD) dataset is conducted. The primary objective is to evaluate the performance in classifying data with respect to efficiency and effectiveness of each algorithm in terms of classification test accuracy, precision, and recall.
Index Terms—Artificial intelligence, machine learning, Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Gaussian Naive Bayes (NB), Support Vector Machines (SVM).
The author is with the Department of Electrical Engineering and Computer Science, University of Toledo, OH 43606 USA (e-mail: firstname.lastname@example.org).
Cite: Ali Al Bataineh, "A Comparative Analysis of Nonlinear Machine Learning Algorithms for Breast Cancer Detection," International Journal of Machine Learning and Computing vol. 9, no. 3, pp. 248-254, 2019.