Medical applications of neural networks: measures of certainty andstatistical tradeoffs
DeLeo, J.M.; Dayhoff, J.E.
Neural Networks, 2001. Proceedings. IJCNN apos;01. International Joint Conference on
Volume 4, Issue , 2001 Page(s):3009 - 3014 vol.4
Digital Object Identifier 10.1109/IJCNN.2001.938857
Summary:We view the output of a classification neural network as a
composite variable that can be subjected to the same kind of statistical
analysis as any other clinical variable used in classification
decisions. We show that receiver operating characteristic (ROC)
methodology, long used in medicine, can be used in neural network
performance evaluation and in sharpening final decisions by adjusting
outputs for prevalence and misclassification costs. We explore the use
of ensembles of neural networks to estimate classification confidence
intervals. Since it is possible to predict outcomes for individual
patients with neural networks, we suggest a paradigm shift from previous
“bin-model” approaches, in which patient outcome and
management decisions are assumed from wide statistical groups into which
the patient fits, to decisions customized to the individual
patient
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