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

Sapitri, Ni Kadek Emik; Sa'adah, Umu; Shofianah, Nur

doi:10.11591/ijai.v14.i2.pp1173-1182

Akses terbuka Jurnal SINTA 1

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

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

DOI: 10.11591/ijai.v14.i2.pp1173-1182
ISSN: 2252-8938, 2089-4872
Terbit: 2025-04-01
Penerbit: Institute of Advanced Engineering and Science
Bahasa: eng
Lisensi: creativecommons.org/licenses/by-sa/4.0

Abstrak

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.

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18 penulis · 38 tautan kepenulisan bersama · 2 lintas organisasi

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University of Brawijaya
Politeknik Perkapalan Negeri Surabaya
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Rekan penulis	Organisasi	Artikel bersama
Hafizha Nur Laily	University of Brawijaya	1
Sobri Abusini	University of Brawijaya	1
Vira Hari Krisnawati	University of Brawijaya	1
Mohammad Dicky Saifullah	University of Brawijaya	1
Kwardiniya Andawaningtyas	University of Brawijaya	1
Endang Wahyu Handamari	University of Brawijaya	1
Agus Suryanto	University of Brawijaya	1
İsnani Darti	University of Brawijaya	1
Onik Febria Damayanti	University of Brawijaya	1
Trisilowati Trisilowati	University of Brawijaya	1
Marsudi	Afiliasi tidak diketahui	1
Wuryansari Muharini Kusumawinahyu	University of Brawijaya	1
Binti Mualifatul Rosydah	Politeknik Perkapalan Negeri Surabaya	1
Syaiful Anam	University of Brawijaya	1
Cynthia Ayu Dwi Lestari	University of Brawijaya	1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.

Metadata asli (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>
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