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Comparison of the Data Classification Approaches to Diagnose Spinal Cord Injury

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Author
Demirer, Rüştü Murat
Arslan, Yunus Ziya
Palamar, Deniz
Uğur, Mukden
Karamehmetoğlu, Şafak Sahir
Type
Article
Date
2012
Language
en_US
Metadata
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Abstract
In our previous study, we have demonstrated that analyzing the skin impedancesmeasured along the key points of the dermatomes might be a useful supplementary technique to enhance the diagnosis of spinal cord injury (SCI), especially for unconscious and noncooperative patients. Initially, in order to distinguish between the skin impedances of control group and patients, artificial neural networks (ANNs) were used as the main data classification approach. However, in the present study, we have proposed two more data classification approaches, that is, support vector machine (SVM) and hierarchical cluster tree analysis (HCTA), which improved the classification rate and also the overall performance. A comparison of the performance of these three methods in classifying traumatic SCI patients and controls was presented. The classification results indicated that dendrogram analysis based on HCTA algorithm and SVM achieved higher recognition accuracies compared to ANN. HCTA and SVM algorithms improved the classification rate and also the overall performance of SCI diagnosis.
Subject
support vector machine
networks
destek vektör makinesi
ağlar
URI
http://hdl.handle.net/11413/1706
Collections
  • Makaleler / Articles [209]
  • Scopus Publications [724]
  • WoS Publications [1016]

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İstanbul Kültür University

Hakkında |Politika | Kütüphane | İletişim | Send Feedback | Admin

Istanbul Kültür University, Ataköy Campus E5 Karayolu Üzeri Bakırköy 34158, İstanbul / TURKEY
Copyright © İstanbul Kültür University

Creative Commons Lisansı
IKU Institutional Repository, Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.

Designed by  UNIREPOS