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...
- 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