Abstract
We introduce and apply a new classification strategy
we call computerized consensus diagnosis (CCD). Its
purpose is to provide robust, reliable classification
of biomedical data. The strategy involves the
cross-validated training of several classifiers of
diverse conceptual and methodological origin on the
same data, and appropriately combining their outcomes.
The strategy is tested on proton magnetic resonance
spectra of human thyroid biopsies, which are
successfully allocated to normal or carcinoma classes.
We used Linear Discriminant Analysis, a Neural
Net-based method, and Genetic Programming as
independent classifiers on two spectral regions, and
chose the median of the six classification outcomes as
the consensus. This procedure yielded 100% specificity
and 100% sensitivity on the training sets, and 100%
specificity and 98% sensitivity on samples of known
malignancy in the test sets. We discuss the necessary
steps any classification approach must take to
guarantee reliability, and stress the importance of
fuzziness and undecidability in robust
classification.
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