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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 |
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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