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ment between AUC and Patient sensitivity as to the

performance of the classification algorithms. The

SVM depends on the kernel parametrization but

together with NB are able to deliver 100% sensitiv-

ity. We find that NB achieved the best performance

with higher AUC and lower FPR. The feature rank-

ing seems a good solution to reduce the dimension

of the dataset but retaining the necessary informa-

tion to get high classification performance.

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