DOI: https://doi.org/10.18517/ijods.1.1.42-50.2020

Classification Breast Cancer Revisited with Machine Learning

Hanna Arini Parhusip (1) , Bambang Susanto (2) , Lilik Linawati (3) , Suryasatriya Trihandaru (4) , Yohanes Sardjono (5) , Adella Septiana Mugirahayu (6)
(1) Mathematics Department, Universitas Kristen Satya Wacana, Jl.Diponegoro 52-60, Salatiga, 50711, Indonesia
(2) Mathematics Department, Universitas Kristen Satya Wacana, Jl.Diponegoro 52-60, Salatiga, 50711, Indonesia
(3) Mathematics Department, Universitas Kristen Satya Wacana, Jl.Diponegoro 52-60, Salatiga, 50711, Indonesia
(4) Mathematics Department, Universitas Kristen Satya Wacana, Jl.Diponegoro 52-60, Salatiga, 50711, Indonesia
(5) BATAN , Jl. Babarsari, Tambak Bayan, Caturtunggal, Kec. Depok, Kabupaten Sleman, Daerah Istimewa Yogyakarta 55281, Indonesia
(6) BATAN , Jl. Babarsari, Tambak Bayan, Caturtunggal, Kec. Depok, Kabupaten Sleman, Daerah Istimewa Yogyakarta 55281, Indonesia
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Keywords:
machine learning, accuracy, breast cancer

Abstract

The article presents the study of several machine learning algorithms that are used to study breast cancer data with 33 features from 569 samples. The purpose of this research is to investigate the best algorithm for classification of breast cancer. The data may have different scales with different large range one to the other features and hence the data are transformed before the data are classified. The used classification methods in machine learning are logistic regression, k-nearest neighbor, Naive bayes classifier, support vector machine, decision tree and random forest algorithm. The original data and the transformed data are classified with size of data test is 0.3. The SVM and Naive Bayes algorithms have no improvement of accuracy with random forest gives the best accuracy among all. Therefore the size of data test is reduced to 0.25 leading to improve all algorithms in transformed data classifications. However, random forest algorithm still gives the best accuracy.

Article Details

How to Cite
[1]
H. A. Parhusip, B. Susanto, L. Linawati, S. Trihandaru, Y. Sardjono, and A. S. Mugirahayu, “Classification Breast Cancer Revisited with Machine Learning”, Int. J. Data. Science., vol. 1, no. 1, pp. 42-50, May 2020.
Issue
Vol 1 No 1 (2020)
Section
Articles

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