A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals

BACKGROUND: Pulmonary acoustic parameters extracted from recorded respiratory sounds provide valuable information for the detection of respiratory pathologies. The automated analysis of pulmonary acoustic signals can serve as a differential diagnosis tool for medical professionals, a learning tool f...

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Detalles Bibliográficos
Autores principales: Palaniappan, Rajkumar, Sundaraj, Kenneth, Sundaraj, Sebastian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4094993/
https://www.ncbi.nlm.nih.gov/pubmed/24970564
http://dx.doi.org/10.1186/1471-2105-15-223

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4094993/
https://www.ncbi.nlm.nih.gov/pubmed/24970564
http://dx.doi.org/10.1186/1471-2105-15-223

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