Application of unsupervised analysis techniques to lung cancer patient data

This study applies unsupervised machine learning techniques for classification and clustering to a collection of descriptive variables from 10,442 lung cancer patient records in the Surveillance, Epidemiology, and End Results (SEER) program database. The goal is to automatically classify lung cancer...

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Detalles Bibliográficos
Autores principales: Lynch, Chip M., van Berkel, Victor H., Frieboes, Hermann B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5598970/
https://www.ncbi.nlm.nih.gov/pubmed/28910336
http://dx.doi.org/10.1371/journal.pone.0184370

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5598970/
https://www.ncbi.nlm.nih.gov/pubmed/28910336
http://dx.doi.org/10.1371/journal.pone.0184370

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