Comparison of the data classification approaches to diagnose spinal cord injury. (English) Zbl 1417.92067
Summary: In [“A quantitative skin impedance test to diagnose spinal cord injury”, Eur. Spine J. 18, No. 7, 972–977 (2009; doi:10.1007/s00586-009-0896-x)], the last author et al. 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 injuries (SCI), especially for unconscious and noncooperative patients. Initially, in order to distinguish between the skin impedances of control groups 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 machines (SVMs) 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 SVMs achieved higher recognition accuracies compared to ANN. HCTA and SVM algorithms improved the classification rate and also the overall performance of SCI diagnosis.
MSC:
| 92C50 | Medical applications (general) |
| 68T05 | Learning and adaptive systems in artificial intelligence |
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
Keywords:
spinal cord injury; diagnosis; data classification; artificial neural networks; support vector machines; hierarchical cluster tree analysisReferences:
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