Exploration of machine learning techniques in predicting multiple sclerosis disease course

OBJECTIVE: To explore the value of machine learning methods for predicting multiple sclerosis disease course. METHODS: 1693 CLIMB study patients were classified as increased EDSS≥1.5 (worsening) or not (non-worsening) at up to five years after baseline visit. Support vector machines (SVM) were used...

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Detalles Bibliográficos
Autores principales: Zhao, Yijun, Healy, Brian C., Rotstein, Dalia, Guttmann, Charles R. G., Bakshi, Rohit, Weiner, Howard L., Brodley, Carla E., Chitnis, Tanuja
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/PMC5381810/
https://www.ncbi.nlm.nih.gov/pubmed/28379999
http://dx.doi.org/10.1371/journal.pone.0174866

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5381810/
https://www.ncbi.nlm.nih.gov/pubmed/28379999
http://dx.doi.org/10.1371/journal.pone.0174866

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