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

被引:96
作者
Agarap, Abien Fred M. [1 ]
机构
[1] Adamson Univ, Dept Comp Sci, Manila, Philippines
来源
2ND INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND SOFT COMPUTING (ICMLSC 2018) | 2015年
关键词
artificial intelligence; artificial neural networks; classification; linear regression; machine learning; multilayer perceptron; nearest neighbors; softmax regression; supervised learning; support vector machine; wisconsin diagnostic breast cancer dataset;
D O I
10.1145/3184066.3184080
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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 approximate to 99.04%.
引用
收藏
页码:5 / 9
页数:5
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