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Breast Cancer Prediction Using Artificial Neural Network

Unnes Journal of Mathematics · Vol. 14 No. 1 (2025) · hlm. 10–15

DOI
10.15294/ujm.v14i1.14212
ISSN
2460-5859, 2252-6943
Terbit
2025-06-14
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

Cancer is one of the medical conditions that causes many deaths in different parts of the world. Based on information obtained from GLOBOCAN, the International Agency for Research on Cancer (IARC) in 2022, there were at least 19.976.499 individuals diagnosed with cancer, and the disease caused death in 9.743.832 people. The detection of breast cancer malignancy relies on the prognosis process, requiring forecasting and automated detection to mitigate diagnostic errors. This facilitates swift and comprehensive analysis of medical data. The study employs the Neural Network, specifically the Artificial Neural Network model, implemented using python and the backpropagation algorithm. Utilizing data from the WDBC Database at the University of Wisconsin, the research achieves a 96,49% accuracy in breast cancer prediction, with an area under curve (AUC) value of 0,992, demonstrating the model's overall efficacy in accurate predictions.

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  • State University of Semarang
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Rekan penulisOrganisasiArtikel bersama
Bambang Eko SusiloState University of Semarang2
Trida Ridho FarizState University of Semarang2
Sri SukaesihState University of Semarang2
Siti WahyuniAfiliasi tidak diketahui2
Aditya MariantiAfiliasi tidak diketahui2
Nuni WidiartiAfiliasi tidak diketahui2
M. Faris Al HakimAfiliasi tidak diketahui1
Karista Gadis SetiyandaAfiliasi tidak diketahui1
M. Faris Al HakimAfiliasi tidak diketahui1
Faith Miftah MaulanaAfiliasi tidak diketahui1

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) https://journal.unnes.ac.id/journals/ujm/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Breast Cancer Prediction Using Artificial Neural Network</dc:title>
	<dc:creator xml:lang="en">Birru Asia Rayani</dc:creator>
	<dc:creator xml:lang="en">Faiza Al Laily Nasron</dc:creator>
	<dc:creator xml:lang="en">Neli Septiana Putri</dc:creator>
	<dc:creator xml:lang="en">Novita Sari Parapat</dc:creator>
	<dc:creator xml:lang="en">Virgania Sari</dc:creator>
	<dc:subject xml:lang="en">breast cancer</dc:subject>
	<dc:subject xml:lang="en">Artificial Neural Network</dc:subject>
	<dc:subject xml:lang="en">backpropagation algorithm</dc:subject>
	<dc:description xml:lang="en">Cancer is one of the medical conditions that causes many deaths in different parts of the world. Based on information obtained from GLOBOCAN, the International Agency for Research on Cancer (IARC) in 2022, there were at least 19.976.499 individuals diagnosed with cancer, and the disease caused death in 9.743.832 people. The detection of breast cancer malignancy relies on the prognosis process, requiring forecasting and automated detection to mitigate diagnostic errors. This facilitates swift and comprehensive analysis of medical data. The study employs the Neural Network, specifically the Artificial Neural Network model, implemented using python and the backpropagation algorithm. Utilizing data from the WDBC Database at the University of Wisconsin, the research achieves a 96,49% accuracy in breast cancer prediction, with an area under curve (AUC) value of 0,992, demonstrating the model's overall efficacy in accurate predictions.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2025-06-14</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">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/ujm/article/view/14212</dc:identifier>
	<dc:identifier>10.15294/ujm.v14i1.14212</dc:identifier>
	<dc:source xml:lang="en">Unnes Journal of Mathematics; Vol. 14 No. 1 (2025): Unnes Journal of Mathematics Volume 1, 2025; 10-15</dc:source>
	<dc:source>2460-5859</dc:source>
	<dc:source>2252-6943</dc:source>
	<dc:source>10.15294/ujm.v14i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/ujm/article/view/14212/4409</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Unnes Journal of Mathematics</dc:rights>
</oai_dc:dc>