Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6533
Title: Diagnosis of Airway Obstruction or Restrictive Spirometric Patterns by Multiclass Support Vector Machines
Authors: Şahin, Deniz
Übeyli, Elif Derya
İlbay, Gül
Şahin, Murat
Yaşar, Alisan Burak
Keywords: Spirometric patterns
Multiclass support vector machine (SVM)
Classification accuracy
Issue Date: 2010
Publisher: Springer
Abstract: This paper presents the use of multiclass support vector machines (SVMs) for diagnosis of spirometric patterns (normal, restrictive, obstructive). The SVM decisions were fused using the error correcting output codes (ECOC). The multiclass SVM with the ECOC was trained on three spirometric parameters (forced expiratory volume in 1s-FEV1, forced vital capacity-FVC and FEV1/FVC ratio). The total classification accuracy of the SVM is 97.32%. The obtained results confirmed the validity of the SVMs to help in clinical decision-making.
URI: https://doi.org/10.1007/s10916-009-9312-7
https://hdl.handle.net/20.500.11851/6533
ISSN: 0148-5598
1573-689X
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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