Heartbeat time series classification with support vector machines
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Prediction of scoliosis curve type based on the analysis of trunk surface topography
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A new multi-task learning technique to predict classification of leukemia and prostate cancer
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
Towards non invasive diagnosis of scoliosis using semi-supervised learning approach
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
Artificial Intelligence in Medicine
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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