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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

8 authors · 15 co-authorship links · 2 across different organisations

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  • Sebelas Maret University
  • Universitas Gadjah Mada
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Herianto HeriantoUniversitas Gadjah Mada2
Yasmin MufidahSebelas Maret University1
Umi SalamahSebelas Maret University1
Esti SuryaniSebelas Maret University1
Sigit SetyawanSebelas Maret University1

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 Okt 2026, last seen 27 Okt 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>