To optimize the sensitivity and specificity performance of a neural network at the same time, a new performance index - the logarithmic-sensitivity index - was introduced. Its ability to identify the optimal stopping point when training with an automated network was compared with the results found when the network was optimized manually. Results show that the log-sensitivity index succeeded in finding a good balance between sensitivity and specificity of the test set and the automated results had a higher mean sensitivity although it was within the error bounds of the manual results. This means that the log-sensitivity index is a valuable timesaving tool, because the networks can be run automatically without user supervision.
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