Performance Enhancement and Accuracy of Artificial Neural Networks Using Particle Swarm Optimization for Breast Cancer Prediction
| Main Authors: | Ginting, Jimmy Nganta, Purba, Ronsen, Panjaitan, Erwin Setiawan |
|---|---|
| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Universitas Islam Sumatera Utara
, 2020
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| Subjects: | |
| Online Access: |
https://jurnal.uisu.ac.id/index.php/infotekjar/article/view/2939 https://jurnal.uisu.ac.id/index.php/infotekjar/article/view/2939/pdf https://jurnal.uisu.ac.id/index.php/infotekjar/article/downloadSuppFile/2939/354 https://jurnal.uisu.ac.id/index.php/infotekjar/article/downloadSuppFile/2939/355 |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title lang="en-US">Performance Enhancement and Accuracy of Artificial Neural Networks Using Particle Swarm Optimization for Breast Cancer Prediction</title><creator>Ginting, Jimmy Nganta</creator><creator>Purba, Ronsen</creator><creator>Panjaitan, Erwin Setiawan</creator><subject lang="en-US">Breast Cancer, Backpropagation Algorithm, Particle Swarm Optimization.</subject><description lang="en-US">Breast cancer is the one of leading causes of death among the women in many parts of the world.  According  to Global Cancer Observatory (GCO) data from WHO (2018) show that approximately 58,256 (16,7%) cancer cases were  found in Indonesia out of a total of 348,809 cancer cases. The number of breast cancer patients throughout the world reached 42.1 per 100,000 population on average death rate of 17 per 100,000 inhabitants.Various ways have been used to find effective methods in the early detection of breast cancer. A prediction of breast cancer in early stage is very important in the medical world, which allows them to develop strategic programs that will help diagnose and reduce mortality rates from breast cancer. Performance enhancement and accuracy of artificial neural networks using particle swarm optimization is an effective solution for breast cancer prediction. The accuracy result was found 70% for training data and 96.1% for 30% prediction in this study. Previous studies only used the backpropagation algorithm to predict breast cancer and the result was 94.17%. Compared with previous study, there is an increase of 1.93% in combining  Backpropagation with Particle Swarm Optimization.</description><publisher lang="en-US">Universitas Islam Sumatera Utara</publisher><contributor lang="en-US"/><date>2020-09-25</date><type>Journal:Article</type><type>Other:info:eu-repo/semantics/publishedVersion</type><type>Journal:Article</type><type>File:application/pdf</type><identifier>https://jurnal.uisu.ac.id/index.php/infotekjar/article/view/2939</identifier><identifier>10.30743/infotekjar.v5i1.2939</identifier><source lang="en-US">InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan; Vol 5, No 1 (2020): InfoTekJar September: In Press; 175-178</source><source>2540-7600</source><source>2540-7597</source><source>10.30743/infotekjar.v5i1</source><language>eng</language><relation>https://jurnal.uisu.ac.id/index.php/infotekjar/article/view/2939/pdf</relation><relation>https://jurnal.uisu.ac.id/index.php/infotekjar/article/downloadSuppFile/2939/354</relation><relation>https://jurnal.uisu.ac.id/index.php/infotekjar/article/downloadSuppFile/2939/355</relation><rights lang="en-US">Copyright (c) 2020 Jimmy Nganta Ginting, Ronsen Purba, Erwin Setiawan Panjaitan</rights><rights lang="en-US">https://creativecommons.org/licenses/by/4.0</rights><recordID>--jurnal.uisu.ac.id-index.php-index-oai:article-2939</recordID></dc>
|
| language |
eng |
| format |
Journal:Article Journal Other:info:eu-repo/semantics/publishedVersion Other File:application/pdf File Journal:eJournal |
| author |
Ginting, Jimmy Nganta Purba, Ronsen Panjaitan, Erwin Setiawan |
| title |
Performance Enhancement and Accuracy of Artificial Neural Networks Using Particle Swarm Optimization for Breast Cancer Prediction |
| publisher |
Universitas Islam Sumatera Utara |
| publishDate |
2020 |
| topic |
Breast Cancer Backpropagation Algorithm Particle Swarm Optimization |
| url |
https://jurnal.uisu.ac.id/index.php/infotekjar/article/view/2939 https://jurnal.uisu.ac.id/index.php/infotekjar/article/view/2939/pdf https://jurnal.uisu.ac.id/index.php/infotekjar/article/downloadSuppFile/2939/354 https://jurnal.uisu.ac.id/index.php/infotekjar/article/downloadSuppFile/2939/355 |
| contents |
Breast cancer is the one of leading causes of death among the women in many parts of the world. According to Global Cancer Observatory (GCO) data from WHO (2018) show that approximately 58,256 (16,7%) cancer cases were found in Indonesia out of a total of 348,809 cancer cases. The number of breast cancer patients throughout the world reached 42.1 per 100,000 population on average death rate of 17 per 100,000 inhabitants.Various ways have been used to find effective methods in the early detection of breast cancer. A prediction of breast cancer in early stage is very important in the medical world, which allows them to develop strategic programs that will help diagnose and reduce mortality rates from breast cancer. Performance enhancement and accuracy of artificial neural networks using particle swarm optimization is an effective solution for breast cancer prediction. The accuracy result was found 70% for training data and 96.1% for 30% prediction in this study. Previous studies only used the backpropagation algorithm to predict breast cancer and the result was 94.17%. Compared with previous study, there is an increase of 1.93% in combining Backpropagation with Particle Swarm Optimization. |
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Universitas Islam Sumatera Utara |
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InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) |
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| subject_area |
Computer Modeling and Simulation/Model dan Simulasi Komputer Computer Communications Networks/Jaringan Komunikasi Komputer Algorithms/Algoritma Computer Security, Data Security/Keamanan Komputer, Keamanan Data |
| city |
KOTA MEDAN |
| province |
SUMATERA UTARA |
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