Breast Cancer Prediction Using Artificial Neural Network

Birru Asia Rayani; Faiza Al Laily Nasron; Neli Septiana Putri; Novita Sari Parapat; Virgania Sari

doi:10.15294/ujm.v14i1.14212

Journal

Breast Cancer Prediction Using Artificial Neural Network

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

DOI: 10.15294/ujm.v14i1.14212
ISSN: 2460-5859, 2252-6943
Published: 2025-06-14
Publisher: Universitas Negeri Semarang
Language: eng

Abstract

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.

Keywords

Collaboration network of the authors

15 authors · 62 co-authorship links

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State University of Semarang
Affiliation unknown

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Same organisation
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Co-author	Organisation	Joint articles
Bambang Eko Susilo	State University of Semarang	2
Trida Ridho Fariz	State University of Semarang	2
Sri Sukaesih	State University of Semarang	2
Siti Wahyuni	Affiliation unknown	2
Aditya Marianti	Affiliation unknown	2
Nuni Widiarti	Affiliation unknown	2
M. Faris Al Hakim	Affiliation unknown	1
Karista Gadis Setiyanda	Affiliation unknown	1
M. Faris Al Hakim	Affiliation unknown	1
Faith Miftah Maulana	Affiliation unknown	1

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.unnes.ac.id/journals/ujm/oai, first harvested 6 Nov 2026, last seen 8 Nov 2026.

Original metadata (oai_dc)

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	<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>
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	<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>
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	<dc:language>eng</dc:language>
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	<dc:rights xml:lang="en">Copyright (c) 2025 Unnes Journal of Mathematics</dc:rights>
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