On breast cancer detection: an application of machine learning algorithms on the wisconsin diagnostic dataset (2017-11-20T00:00:00.000000Z)

TL;DR

A comparison of six machine learning algorithms: GRU-SVM, Linear Regression, Multilayer Perceptron (MLP), Nearest Neighbor (NN) search, Softmaxregression, and Support Vector Machine on the Wisconsin Diagnostic Breast Cancer dataset by measuring their classification test accuracy, and their sensitivity and specificity values.

Abstract

This paper presents a comparison of six machine learning (ML) algorithms: GRU-SVM[1], Linear Regression, Multilayer Perceptron (MLP), Nearest Neighbor (NN) search, Softmax Regression, and Support Vector Machine (SVM) on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset[2] 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[2]. 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%.

Authors

Abien Fred Agarap

4 papers

On breast cancer detection: an application of machine learning algorithms on the wisconsin diagnostic dataset

TL;DR

Abstract

Authors

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