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We have developed a multi-probe resonance-frequency electrical impedance spectroscope (REIS) system to detect breast
abnormalities. Based on assessing asymmetry in REIS signals acquired between left and right breasts, we developed
several machine learning classifiers to classify younger women (i.e., under 50YO) into two groups of having high and
low risk for developing breast cancer. In this study, we investigated a new method to optimize performance based on the
area under a selected partial receiver operating characteristic (ROC) curve when optimizing an artificial neural network
(ANN), and tested whether it could improve classification performance. From an ongoing prospective study, we selected
a dataset of 174 cases for whom we have both REIS signals and diagnostic status verification. The dataset includes 66
"positive" cases recommended for biopsy due to detection of highly suspicious breast lesions and 108 "negative" cases
determined by imaging based examinations. A set of REIS-based feature differences, extracted from the two breasts
using a mirror-matched approach, was computed and constituted an initial feature pool. Using a leave-one-case-out
cross-validation method, we applied a genetic algorithm (GA) to train the ANN with an optimal subset of features. Two
optimization criteria were separately used in GA optimization, namely the area under the entire ROC curve (AUC) and
the partial area under the ROC curve, up to a predetermined threshold (i.e., 90% specificity). The results showed that
although the ANN optimized using the entire AUC yielded higher overall performance (AUC = 0.83 versus 0.76), the
ANN optimized using the partial ROC area criterion achieved substantially higher operational performance (i.e.,
increasing sensitivity level from 28% to 48% at 95% specificity and/ or from 48% to 58% at 90% specificity).
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