Revisiting the area under the ROC

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Proceedings titleStudies in Health Technology and Informatics
Conference23rd International Conference of the European Federation for Medical Informatics, MIE 2011, 28 August 2011 through 31 August 2011, Oslo
Pages532536; # of pages: 5
Subjectalgorithm; area under the curve; automated pattern recognition; computer assisted diagnosis; conference paper; fracture; human; methodology; radiography; radiology; receiver operating characteristic; reproducibility; sensitivity and specificity; statistical analysis; statistical model; statistics; Algorithms; Area Under Curve; Data Interpretation, Statistical; Fractures, Bone; Humans; Models, Statistical; Pattern Recognition, Automated; Radiographic Image Interpretation, Computer-Assisted; Radiology; Reproducibility of Results; ROC Curve; Sensitivity and Specificity; Statistics as Topic
AbstractThe Receiver-Operating Characteristic curve or ROC has been a long standing and well appreciated tool to assess performance of classifiers or diagnostic tests. Likewise, the Area Under the ROC (AUC) has been a metric to summarize the power of a test or ability of a classifier in one measurement. This article aims to revisit the AUC, and ties it to key characteristics of the noncentral hypergeometric distribution. It is demonstrated that this statistical distribution can be used in modeling the behaviour of classifiers, which is of value for comparing classifiers. © 2011 European Federation for Medical Informatics. All rights reserved.
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AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Information Technology (IIT-ITI)
Peer reviewedYes
NPARC number21271368
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Record identifieree17c0b2-8732-4f2c-8af5-a2de64f17f9f
Record created2014-03-24
Record modified2016-05-09
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