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

Abien Fred Agarap
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引用次数: 186

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%.
乳腺癌检测:机器学习算法在威斯康星诊断数据集上的应用
本文比较了六种机器学习(ML)算法:GRU-SVM[1]、线性回归(Linear Regression)、多层感知器(Multilayer Perceptron, MLP)、最近邻(Nearest Neighbor, NN)搜索、Softmax Regression和支持向量机(Support Vector machine, SVM)在威斯康星乳腺癌诊断(WDBC)数据集[2]上的分类测试准确率,以及它们的灵敏度和特异性值。该数据集包括从乳腺肿块的FNA测试的数字化图像中计算得出的特征[2]。对于ML算法的实现,数据集以以下方式划分:70%用于训练阶段,30%用于测试阶段。所有分类器使用的超参数都是手动分配的。结果表明,所提出的ML算法在分类任务上表现良好(均超过90%的测试准确率)。MLP算法在已实现的算法中脱颖而出,测试准确率达到约99.04%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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