Title: On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset,
Abstract: This paper presents a comparison of six machine learning (ML) algorithms:
GRU-SVM (Agarap, 2017), Linear Regression, Multilayer Perceptron (MLP), Nearest
Neighbor (NN) search, Softmax Regression, and Support Vector Machine (SVM) on
the Wisconsin Diagnostic Breast Cancer (WDBC) dataset (Wolberg, Street, &
Mangasarian, 1992) by measuring their classification test accuracy and their
sensitivity and specificity values. The said dataset consists of features which
were computed from digitized images of FNA tests on a breast mass (Wolberg,
Street, & Mangasarian, 1992). For the implementation of the ML algorithms, the
dataset was partitioned in the following fashion: 70% for training phase, and
30% for the testing phase. The hyper-parameters used for all the classifiers
were manually assigned. Results show that all the presented ML algorithms
performed well (all exceeded 90% test accuracy) on the classification task. The
MLP algorithm stands out among the implemented algorithms with a test accuracy
of ~99.04%.