Scopus
YÖKSİS DOI Eşleşti
SJR Q1
Assessment of exercise stress testing with artificial neural network in determining coronary artery disease and predicting lesion localization
Expert Systems with Applications · Ocak 2009
YÖKSİS Kayıtları
Assessment of exercise stress testing with artificial neural network in determining coronary artery disease and predicting lesion localization
Expert Systems with Applications · 2009 SCI-Expanded
Prof. Dr. NAZİF AYGÜL →
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Assessment of exercise stress testing with artificial neural network in determining coronary artery disease and predicting lesion localization
2009 ISSN: 09574174 SCI-Expanded
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Makale Bilgileri
ISSN09574174
Yayın TarihiOcak 2009
Cilt / Sayfa36 · 2562-2566
Scopus ID2-s2.0-56349166799
Özet
The aim of this study is to show the artificial neural network (ANN) on determination of coronary artery disease existence and localization of lesion based upon exercise stress testing (EST) data. EST and coronary angiography were performed on 330 patients. The data studied acquiring 27 verifying features was normalized employing z-score method. To select training and test data, 10-fold cross-validation methods were involved and multi-layered perceptron neural network was employed for the classification. The interpretation of EST using ANN proved 91%, 73% and 65% diagnostic accuracy for the left main coronary (LMCA), left anterior descending and left circumflex coronary arteries, respectively. Besides, 69% for the right coronary artery is also predicted. For the LMCA, a 94% negative predictive value (NPV) was obtained. This high percentage of NPV encourages the elimination of LMCA lesions. Some knowledge can also be obtained about lesion localization, besides diagnosing of coronary artery disease by the assessment of EST via ANN. © 2007 Elsevier Ltd. All rights reserved.
Yazarlar (5)
1
Ismail Babaoǧlu
2
Omer Kaan Baykan
3
Nazif Aygül
4
Kurtuluş Özdemir
5
Mehmet Bayrak
Anahtar Kelimeler
Artificial neural networks
Coronary artery disease
Exercise stress testing
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Expert Systems with Applications
Q1
SJR Skoru1,854
H-Index290
YayıncıElsevier Ltd
ÜlkeUnited Kingdom
Artificial Intelligence (Q1)
Computer Science Applications (Q1)
Engineering (miscellaneous) (Q1)
Metrikler
36
Atıf
5
Yazar
3
Anahtar Kelime