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A support vector machines classifier to assess the severity of idiopathic scoliosis from surface topography
Ramirez, L.; Durdle, N.G.; Raso, V.J.; Hill, D.L.;
Information Technology in Biomedicine, IEEE Transactions on
Volume 10,
Issue 1,
Jan. 2006
Page(s):84
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91
Abstract:
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.
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