Exploration of machine learning techniques in predicting multiple sclerosis disease course

PloS one, Volume 12, Issue 4, 2017.

Cited by: 19|Bibtex|Views5|DOI:https://doi.org/10.1371/journal.pone.0174866
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Other Links: pubmed.ncbi.nlm.nih.gov|academic.microsoft.com

Abstract:

SVM incorporating short-term clinical and brain MRI data, class imbalance corrective measures, and classification costs may be a promising means to predict MS disease course, and for selection of patients suitable for more aggressive treatment regimens.

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