White box radial basis function classifiers with component selection for clinical prediction models

Artificial Intelligence in Medicine

Volumn 60, Issue 1, 2014, Pages 53-64

  Van Belle, Vanya ^a,b   Lisboa, Paulo ^b  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b LIVERPOOL JOHN MOORES UNIVERSITY   (United Kingdom)

Author keywords

Clinical decision support; Feature selection; Interpretable support vector machines; Radial basis functions; White box methods

Indexed keywords

CLINICAL DECISION SUPPORT; LOGISTIC REGRESSION MODELING; RADIAL BASIS FUNCTION CLASSIFIERS; RADIAL BASIS FUNCTIONS; RECEIVER OPERATING CHARACTERISTIC CURVES; SUPPORT VECTOR MACHINE (SVMS); WHITE BOX; WISCONSIN BREAST CANCER DATASET;

ARTIFICIAL INTELLIGENCE; DECISION SUPPORT SYSTEMS; FEATURE EXTRACTION; IMAGE SEGMENTATION; MATHEMATICAL MODELS; RADIAL BASIS FUNCTION NETWORKS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES;

ITERATIVE METHODS;

GLUCOSE;

ACCURACY; ADULT; ALGORITHM; AREA UNDER THE CURVE; ARTICLE; BODY MASS; CARDIOVASCULAR DISEASE; DECISION MAKING; DECISION SUPPORT SYSTEM; DIABETES MELLITUS; FEMALE; GLUCOSE BLOOD LEVEL; HUMAN; KERNEL METHOD; MAJOR CLINICAL STUDY; PREDICTION; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SUPPORT VECTOR MACHINE; YOUNG ADULT;

CLINICAL DECISION SUPPORT; FEATURE SELECTION; INTERPRETABLE SUPPORT VECTOR MACHINES; RADIAL BASIS FUNCTIONS; WHITE BOX METHODS;

FREE RADICALS; MODELS, BIOLOGICAL;

EID: 84892896473     PISSN: 09333657     EISSN: 18732860     Source Type: Journal    
DOI: 10.1016/j.artmed.2013.10.001     Document Type: Article

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