A support vectors classifier approach to predicting the risk of progression of adolescent idiopathic scoliosis

IEEE Transactions on Information Technology in Biomedicine

Volumn 9, Issue 2, 2005, Pages 276-282

  Ajemba, Peter O ^a   Ramirez, Lino ^a   Durdle, Nelson G ^a   Hill, Doug L ^b   Raso, V James ^b  

^a UNIVERSITY OF ALBERTA   (Canada)

^b GLENROSE REHABILITATION HOSPITAL   (Canada)

Author keywords

Decision support systems; Lenke indicators; Machine learning (ML); Scoliosis progression; Support vector classifiers (SVCs)

Indexed keywords

ARTIFICIAL INTELLIGENCE; HEALTH CARE; LEARNING SYSTEMS; MATHEMATICAL MODELS; MATRIX ALGEBRA; OPTIMIZATION; REGRESSION ANALYSIS; RISKS; STATISTICAL METHODS;

LENKE INDICATORS; LINEAR REGRESSION MODEL; SCOLIOSIS PROGRESSION; SUPPORT VECTOR CLASSIFIERS (SVCS);

DECISION SUPPORT SYSTEMS;

ADOLESCENT; ARTICLE; CLASSIFICATION; DECISION SUPPORT SYSTEM; DISEASE COURSE; FEMALE; HUMAN; MALE; PATHOPHYSIOLOGY; RETROSPECTIVE STUDY; SCOLIOSIS;

ADOLESCENT; DECISION SUPPORT SYSTEMS, CLINICAL; DISEASE PROGRESSION; FEMALE; HUMANS; MALE; RETROSPECTIVE STUDIES; SCOLIOSIS;

EID: 21444454271     PISSN: 10897771     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITB.2005.847169     Document Type: Article

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