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 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.

• Autores: Soto Vergel, Angelo Joseph; Medina Delgado, Byron; palacios alvarado, wlamyr
• Tipo de recurso: Article of investigation
• Fecha de publicación: 2021
• Institución: UNIVERSIDAD FRANCISCO DE PAULA SANTANDER
• Repositorio: Repositorio Digital UFPS
• Idioma: eng
• OAI Identifier: oai:repositorio.ufps.edu.co:ufps/6539
• Acceso en línea: https://repositorio.ufps.edu.co/handle/ufps/6539
• Palabra clave: Chromatography; Classification (of information); Diagnosis; Diseases; Feature extraction; Noninvasive medical procedures; Signal processing; Urology; Auto regressive models; Autoregressive coefficient; Autoregressive modelling; Chromatographic signals; Feature extraction methods; Non-invasive diagnostics; Noninvasive methods; Research questions; Extraction
• Rights: openAccess
• License: Content from this work may be used under the terms of theCreative Commons Attribution 3.0 licence