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PLoS One. 2017 Apr 5;12(4):e0174866. doi: 10.1371/journal.pone.0174866. eCollection 2017.

Exploration of machine learning techniques in predicting multiple sclerosis disease course.

Author information

1
Department of Computer Science, Tufts University, Medford, Massachusetts, United States of America.
2
Partners MS Center, Brigham and Women's Hospital, Brookline, Massachusetts, United States of America.
3
Biostatistics Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
4
College of Computer and Information Science, Northeastern, Boston, Massachusetts, United States of America.

Abstract

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 to build the classifier, and compared to logistic regression (LR) using demographic, clinical and MRI data obtained at years one and two to predict EDSS at five years follow-up.

RESULTS:

Baseline data alone provided little predictive value. Clinical observation for one year improved overall SVM sensitivity to 62% and specificity to 65% in predicting worsening cases. The addition of one year MRI data improved sensitivity to 71% and specificity to 68%. Use of non-uniform misclassification costs in the SVM model, weighting towards increased sensitivity, improved predictions (up to 86%). Sensitivity, specificity, and overall accuracy improved minimally with additional follow-up data. Predictions improved within specific groups defined by baseline EDSS. LR performed more poorly than SVM in most cases. Race, family history of MS, and brain parenchymal fraction, ranked highly as predictors of the non-worsening group. Brain T2 lesion volume ranked highly as predictive of the worsening group.

INTERPRETATION:

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.

PMID:
28379999
PMCID:
PMC5381810
DOI:
10.1371/journal.pone.0174866
[Indexed for MEDLINE]
Free PMC Article

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