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Backward Elimination for Feature Selection on Breast Cancer Classification Using Logistic Regression and Support Vector Machine Algorithms

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 17 No. 4 (2023) · pp. 429–440

DOI
10.22146/ijccs.88926
ISSN
2460-7258, 1978-1520
Published
2023-10-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Breast cancer is a prevalent form of cancer that afflicts women across all nations globally. One of the ways that can be done as a prevention to reduce elevated fatality due to breast cancer is with a detection system that can determine whether a cancer is benign or malignant. Logistic Regression and Support Vector Machine (SVM) classification algorithms are often used to detect this disease, but the use of these two algorithms often doesn’t give optimal results when applied to datasets that have many features, so additional algorithm is needed to improve classification performance by using Backward Elimination feature selection. The comparison of Logistic Regression and SVM algorithms was carried out by applying feature selection to breast cancer data to see the best model. The breast cancer dataset has 30 features and two classes, Benign and Malignant. Backward Elimination has reduced features from 30 features to 13 features, thereby increasing the performance of both classification models. The best classification was obtained by using the Backward Elimination feature selection and linear kernel SVM with an increase in accuracy value from 96.14% to 97.02%, precision from 98.06% to 99.49%, recall from 90.48% to 92.38%, and the AUC from 0.95 to 0.96.

Keywords

Collaboration network of the authors

30 authors · 102 co-authorship links · 4 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Lambung Mangkurat University
  • Universitas Mercu Buana Yogyakarta
  • Diponegoro University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Friska AbadiLambung Mangkurat University7
Muhammad Itqan MazdadiLambung Mangkurat University6
Andi FarmadiLambung Mangkurat University5
Irwan BudimanLambung Mangkurat University4
Mohammad Reza FaisalLambung Mangkurat University4
Nitami Lestari PutriDiponegoro University2
Deni KurniaLambung Mangkurat University2
Siti Roziana AzizahLambung Mangkurat University2
Reina Alya RahmaLambung Mangkurat University2
Adela Putri AriyantiLambung Mangkurat University1
Muliadi MuliadiLambung Mangkurat University1
Muhammad Hevny RizkyLambung Mangkurat University1
Fenny Winda RahayuLambung Mangkurat University1
Dodon Turianto NugrahadiLambung Mangkurat University1
Muliadi MuliadiLambung Mangkurat University1
Sri RedjekiAffiliation unknown1
Annisa Salsabila AhdyaniLambung Mangkurat University1
Muhammad Amir NugrahaLambung Mangkurat University1
MuliadiAffiliation unknown1
Putri NabellaAffiliation unknown1
Setyo Wahyu SaputroLambung Mangkurat University1
Putri NabellaLambung Mangkurat University1
Muhammad FikriUniversitas Mercu Buana Yogyakarta1
Radityo Adi NugrohoAffiliation unknown1
Setyo Wahyu SaputroAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Backward Elimination for Feature Selection on Breast Cancer Classification Using Logistic Regression and Support Vector Machine Algorithms</dc:title>
	<dc:creator>Farahdiba, Salsha</dc:creator>
	<dc:creator>Kartini, Dwi</dc:creator>
	<dc:creator>Nugroho, Radityo Adi</dc:creator>
	<dc:creator>Herteno, Rudy</dc:creator>
	<dc:creator>Saragih, Triando Hamonangan</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science; Data Science; Machine Learning</dc:subject>
	<dc:subject xml:lang="en-US">Breast Cancer Classification; Backward Elimination; Logistic Regression; SVM</dc:subject>
	<dc:description xml:lang="en-US">Breast cancer is a prevalent form of cancer that afflicts women across all nations globally. One of the ways that can be done as a prevention to reduce elevated fatality due to breast cancer is with a detection system that can determine whether a cancer is benign or malignant. Logistic Regression and Support Vector Machine (SVM) classification algorithms are often used to detect this disease, but the use of these two algorithms often doesn’t give optimal results when applied to datasets that have many features, so additional algorithm is needed to improve classification performance by using Backward Elimination feature selection. The comparison of Logistic Regression and SVM algorithms was carried out by applying feature selection to breast cancer data to see the best model. The breast cancer dataset has 30 features and two classes, Benign and Malignant. Backward Elimination has reduced features from 30 features to 13 features, thereby increasing the performance of both classification models. The best classification was obtained by using the Backward Elimination feature selection and linear kernel SVM with an increase in accuracy value from 96.14% to 97.02%, precision from 98.06% to 99.49%, recall from 90.48% to 92.38%, and the AUC from 0.95 to 0.96.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2023-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/88926</dc:identifier>
	<dc:identifier>10.22146/ijccs.88926</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 17, No 4 (2023): October; 429-440</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/88926/37714</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>