Abstract

To benefit from the full information content of the mid-IR spectra of human sera, we directly related the overall shape of the spectra to the donors’ disease states. For this approach of disease pattern recognition we applied cluster analysis and discriminant analysis to the example of the disease states diabetes type 1, diabetes type 2, and healthy. In a binary, supervised classification of any pair of these disease states we achieved specificities and sensitivities of ∼80% within our data set.

© 2000 Optical Society of America

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