Autoregressive modelling of chromatographic signals from urine samples for prostate cancer diagnosis
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Soto Vergel, Angelo Joseph | 2021-06-09
This article evaluates autoregressive modeling as a feature extraction method in a
database of chromatographic signals from urine samples for non-invasive diagnostic support of
prostate cancer in response to the research question: Can chromatographic signals from urine
be characterized and used as a non-invasive method for cancer diagnosis? For this purpose, a
database of 18 patients, 9 diagnosed with prostate cancer and 9 control patients, is
consolidated, statistical methods are implemented to generate autoregressive coefficients from
the data signals, and finally, the principal component analysis technique is applied for crossclass classification. As a result, a correct classification was obtained in the total number of
samples validating the autoregressive modelling as a feature extraction method in contrast to
the conventional methodology usually followed in chromatographic signal processing.
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