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This paper presents a comparison of six machine learning (ML) algorithms:GRU-SVM (Agarap, 2017), Linear Regression, Multilayer Perceptron (MLP), NearestNeighbor (NN) search, Softmax Regression, and Support Vector Machine (SVM) onthe Wisconsin Diagnostic Breast Cancer (WDBC) dataset (Wolberg, Street, &Mangasarian, 1992) by measuring their classification test accuracy and theirsensitivity and specificity values. The said dataset consists of features whichwere computed from digitized images of FNA tests on a breast mass (Wolberg,Street, & Mangasarian, 1992). For the implementation of the ML algorithms, thedataset was partitioned in the following fashion: 70% for training phase, and30% for the testing phase. The hyper-parameters used for all the classifierswere manually assigned. Results show that all the presented ML algorithmsperformed well (all exceeded 90% test accuracy) on the classification task. TheMLP algorithm stands out among the implemented algorithms with a test accuracyof ~99.04%.

On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset