Autoregressive modelling of chromatographic signals from urine samples for prostate cancer diagnosis

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.

• Autores: Medina Delgado, Byron; Soto Vergel, Angelo Joseph; PALACIOS ALVARADO, WLAMYR
• Tipo de recurso: Article of journal
• Fecha de publicación: 2021
• Institución: Universidad Francisco de Paula Santander
• Repositorio: Repositorio Digital UFPS
• Idioma: eng
• OAI Identifier: oai:repositorio.ufps.edu.co:ufps/781
• Acceso en línea: http://repositorio.ufps.edu.co/handle/ufps/781; https://doi.org/10.1088/1742-6596/1938/1/012011
• Rights: openAccess
• License: Atribución 4.0 Internacional (CC BY 4.0)