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The Analysis of Performace Model Tiered Artificial Neural Network for Assessment of Coronary Heart Disease

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 7 No. 4 (2017)

DOI
10.11591/ijece.v7i4.pp2183-2191
ISSN
2088-8708, 2088-8708
Published
2017-08-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

The assessment model of coronary heart disease is so much developed in line with the development of information technology, particularly the field of artificial intelligence. Unfortunately, the assessment models developed mostly do not use such an approach made by the clinician, the tiered approach. This study aims to analyze the performance of a tiered model assessment. The method used for each level is, preprocessing, building architecture artificial neural network (ANN), conduct training using the Levenberg-Marquardt algorithm and one step secant, as well as testing the system. The study is divided into the terms of the stages in the examination procedure. The test results showed the influence of each level, both when the output level of the previous positive or negative, were tested back at the next level. The performance evaluation may indicate that the top level provides performance improvement and or reinforce the previous level.

Collaboration network of the authors

40 authors · 143 co-authorship links · 3 across different organisations · showing the 40 most connected

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  • Sebelas Maret University
  • Universitas Gadjah Mada
  • State University of Semarang
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Esti SuryaniSebelas Maret University3
Yasmin MufidahSebelas Maret University2
Herianto HeriantoUniversitas Gadjah Mada2
Umi SalamahSebelas Maret University1
Sigit SetyawanSebelas Maret University1
Bambang PriyonoState University of Semarang1
Dibyo PramonoAffiliation unknown1
Al. Supartinah SantosoAffiliation unknown1
Rini AnggrainingsihAffiliation unknown1
Firdaus Ashiddqi NoorAffiliation unknown1
Gita Bangun PrakosoAffiliation unknown1
Dewi WardaniAffiliation unknown1
Hasan Dwi CahyonoAffiliation unknown1
Angga Exca Pradipta SyaifuddinAffiliation unknown1
Dafina NazhifahAffiliation unknown1
Fajar MuslimAffiliation unknown1
Jontari HutagalungAffiliation unknown1
S. SupargiyonoAffiliation unknown1
P. PurwonoAffiliation unknown1
Sadewa Ahmad HamimAffiliation unknown1
Darojatun IdaAffiliation unknown1
Satyagraha Ari WinastiAffiliation unknown1
Novijanti RintisAffiliation unknown1
Triwibowo A. GarjitoAffiliation unknown1
Mega Tyas PrihatinAffiliation unknown1
Bai AprisAffiliation unknown1
Bansai ImmanuelAffiliation unknown1
Kik Hao SamuelAffiliation unknown1
Hananta LinawatiAffiliation unknown1
Vicka CahyawatiAffiliation unknown1
Farah Nada MufidahAffiliation unknown1
Muchsin MaulanaAffiliation unknown1
Agus SuwarniAffiliation unknown1
Gurdani YogisutantiAffiliation unknown1
Lientje Setyawati MauritsAffiliation unknown1
Bagus RiyonoUniversitas Gadjah Mada1
Utami UtamiAffiliation unknown1

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.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Oct 2026, last seen 8 Nov 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>The Analysis of Performace Model Tiered Artificial Neural Network for Assessment of Coronary Heart Disease</dc:title><dc:creator>Wiharto, Wiharto</dc:creator><dc:creator>Herianto, Harianto</dc:creator><dc:creator>Kusnanto, Hari</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;p&amp;gt;The assessment model of coronary heart disease is so much developed in line with the development of information technology, particularly the field of artificial intelligence. Unfortunately, the assessment models developed mostly do not use such an approach made by the clinician, the tiered approach. This study aims to analyze the performance of a tiered model assessment. The method used for each level is, preprocessing, building architecture artificial neural network (ANN), conduct training using the Levenberg-Marquardt algorithm and one step secant, as well as testing the system. The study is divided into the terms of the stages in the examination procedure. The test results showed the influence of each level, both when the output level of the previous positive or negative, were tested back at the next level. The performance evaluation may indicate that the top level provides performance improvement and or reinforce the previous level. &amp;lt;/p&amp;gt;&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2017-08-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v7i4.pp2183-2191</dc:identifier><dc:identifier>10.11591/ijece.v7i4.pp2183-2191</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 7 No 4 (2017); 2183</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/7760/7245</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>