Open AccessAutoregressive modelling of chromatographic signals from urine samples for prostate cancer diagnosis
Author(s) -
Byron Medina Delgado,
Ángelo Joseph Soto Vergel,
Wlamyr Palacios Alvarado
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1938/1/012011
Subject(s) - autoregressive model , principal component analysis , prostate cancer , pattern recognition (psychology) , feature extraction , computer science , urine , artificial intelligence , feature (linguistics) , cancer , chromatography , statistics , mathematics , chemistry , medicine , linguistics , philosophy
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 cross-class 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.