Medical applications of neural networks: measures of certainty and statistical tradeoffs

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