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A support vector machines classifier to assess the severity of idiopathic scoliosis from surface topography

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4 Author(s)
Ramirez, L. ; Dept. of Electr. & Comput. Eng., Univ. of Alberta, Edmonton, Alta. ; Durdle, N.G. ; Raso, V.J. ; Hill, D.L.

A support vector machines (SVM) classifier was used to assess the severity of idiopathic scoliosis (IS) based on surface topographic images of human backs. Scoliosis is a condition that involves abnormal lateral curvature and rotation of the spine that usually causes noticeable trunk deformities. Based on the hypothesis that combining surface topography and clinical data using a SVM would produce better assessment results, we conducted a study using a dataset of 111 IS patients. Twelve surface and clinical indicators were obtained for each patient. The result of testing on the dataset showed that the system achieved 69-85% accuracy in testing. It outperformed a linear discriminant function classifier and a decision tree classifier on the dataset

Published in:

Information Technology in Biomedicine, IEEE Transactions on  (Volume:10 ,  Issue: 1 )

Date of Publication:

Jan. 2006

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