TY - NEWS
TI - Autoregressive modelling of chromatographic signals from urine samples for prostate cancer diagnosis
AU - Soto Vergel, Angelo Joseph
AU - Medina Delgado, Byron
AU - palacios alvarado, wlamyr
AB - 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.
DA - 2021-06-09
KW - Chromatography
KW - Classification (of information)
KW - Diagnosis
KW - Diseases
KW - Feature extraction
KW - Noninvasive medical procedures
KW - Signal processing
KW - Urology
KW - Auto regressive models
KW - Autoregressive coefficient
KW - Autoregressive modelling
KW - Chromatographic signals
KW - Feature extraction methods
KW - Non-invasive diagnostics
KW - Noninvasive methods
KW - Research questions
KW - Extraction
PB - Journal of Physics: Conference Series
UR - https://repositorio.ufps.edu.co/handle/ufps/6539
ER -