Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 803980, 7 pages
http://dx.doi.org/10.1155/2012/803980
Research Article
Comparison of the Data Classification Approaches to Diagnose Spinal Cord Injury
1Department of Mechanical Engineering, Faculty of Engineering, Istanbul University, Avcilar, 34320 Istanbul, Turkey
2Department of Mathematics and Computer Science, Istanbul Kultur University, Sirinevler, 34156 Istanbul, Turkey
3Department of Physical Medicine and Rehabilitation, Kars State Hospital, 36000 Kars, Turkey
4Department of Electrical & Electronics Engineering, Faculty of Engineering, Istanbul University, Avcilar, 34320 Istanbul, Turkey
5Department of Physical Medicine and Rehabilitation, Cerrahpasa Medical Faculty, Istanbul University, Cerrahpasa, 34098 Istanbul, Turkey
Received 14 September 2011; Revised 23 November 2011; Accepted 20 December 2011
Academic Editor: Bill Crum
Copyright © 2012 Yunus Ziya Arslan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
In our previous study, we have demonstrated that analyzing the skin impedances measured 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.