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Hybrid methods to identify ovarian cancer from imbalanced high-dimensional microarray data

IAES International Journal of Artificial Intelligence · Vol. 14 No. 2 (2025) · pp. 1173–1182

DOI
10.11591/ijai.v14.i2.pp1173-1182
ISSN
2252-8938, 2089-4872
Published
2025-04-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Scientists have used microarray data to identify healthy people and patients with various types of cancer, including ovarian cancer. Ovarian cancer is the most dangerous of all types of cancer that attacks the female reproductive organ. The right combination of methods is needed to identify ovarian cancer from microarray data because that type of data is high-dimensional and imbalanced. This research aims to propose two hybrid methods which are a combination of infinite feature selection (IFS) as features selector with classification and regression tree (CART) as a classifier. IFS can work with two separate scenarios, namely supervised infinite feature selection (SIFS) and unsupervised infinite feature selection (UIFS). This research also compares the performance of the two hybrid methods proposed (SIFS-CART and UIFS-CART) with CART without IFS. The data used is OVA_ovary that has 10937 columns and 1545 rows. The results shows that SIFS-CART achieves maximum performance using 1000 features and UIFS-CART 5000 features. CART without IFS uses all 10935 features. The balanced accuracy results show SIFS-CART can outperform CART without IFS and UIFS-CART. Using less features to get highest balanced accuracy results, SIFS is more effective in performing feature selection on the OVA_ovary dataset compared to UIFS.

Keywords

Collaboration network of the authors

18 authors · 38 co-authorship links · 2 across different organisations

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

  • University of Brawijaya
  • Politeknik Perkapalan Negeri Surabaya
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Hafizha Nur LailyUniversity of Brawijaya1
Sobri AbusiniUniversity of Brawijaya1
Vira Hari KrisnawatiUniversity of Brawijaya1
Mohammad Dicky SaifullahUniversity of Brawijaya1
Kwardiniya AndawaningtyasUniversity of Brawijaya1
Endang Wahyu HandamariUniversity of Brawijaya1
Agus SuryantoUniversity of Brawijaya1
İsnani DartiUniversity of Brawijaya1
Onik Febria DamayantiUniversity of Brawijaya1
Trisilowati TrisilowatiUniversity of Brawijaya1
MarsudiAffiliation unknown1
Wuryansari Muharini KusumawinahyuUniversity of Brawijaya1
Binti Mualifatul RosydahPoliteknik Perkapalan Negeri Surabaya1
Syaiful AnamUniversity of Brawijaya1
Cynthia Ayu Dwi LestariUniversity of Brawijaya1

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) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Hybrid methods to identify ovarian cancer from imbalanced high-dimensional microarray data</dc:title>
	<dc:creator>Sapitri, Ni Kadek Emik</dc:creator>
	<dc:creator>Sa'adah, Umu</dc:creator>
	<dc:creator>Shofianah, Nur</dc:creator>
	<dc:subject xml:lang="en-US">Machine learning; decision tree; graph theory; feature selection algorithm; hybrid method;</dc:subject>
	<dc:subject xml:lang="en-US">Balanced accuracy; CART; Hybrid method; Infinite feature selection; Microarray data; Ovarian cancer;</dc:subject>
	<dc:description xml:lang="en-US">Scientists have used microarray data to identify healthy people and patients with various types of cancer, including ovarian cancer. Ovarian cancer is the most dangerous of all types of cancer that attacks the female reproductive organ. The right combination of methods is needed to identify ovarian cancer from microarray data because that type of data is high-dimensional and imbalanced. This research aims to propose two hybrid methods which are a combination of infinite feature selection (IFS) as features selector with classification and regression tree (CART) as a classifier. IFS can work with two separate scenarios, namely supervised infinite feature selection (SIFS) and unsupervised infinite feature selection (UIFS). This research also compares the performance of the two hybrid methods proposed (SIFS-CART and UIFS-CART) with CART without IFS. The data used is OVA_ovary that has 10937 columns and 1545 rows. The results shows that SIFS-CART achieves maximum performance using 1000 features and UIFS-CART 5000 features. CART without IFS uses all 10935 features. The balanced accuracy results show SIFS-CART can outperform CART without IFS and UIFS-CART. Using less features to get highest balanced accuracy results, SIFS is more effective in performing feature selection on the OVA_ovary dataset compared to UIFS.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">Lembaga Pengelola Dana Pendidikan (LPDP).</dc:contributor>
	<dc:date>2025-04-01</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://ijai.iaescore.com/index.php/IJAI/article/view/25480</dc:identifier>
	<dc:identifier>10.11591/ijai.v14.i2.pp1173-1182</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 14, No 2: April 2025; 1173-1182</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v14.i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/25480/14447</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>