title: Exploration of machine learning techniques in predicting multiple sclerosis disease course.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Computer Science, Tufts University, Medford, Massachusetts, United States of America.
authors.forename: Yijun
authors.initials: Y
authors.lastname: Zhao
authors.affiliation: Partners MS Center, Brigham and Women's Hospital, Brookline, Massachusetts, United States of America.|Biostatistics Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
authors.forename: Brian C
authors.initials: BC
authors.lastname: Healy
authors.affiliation: Partners MS Center, Brigham and Women's Hospital, Brookline, Massachusetts, United States of America.
authors.forename: Dalia
authors.initials: D
authors.lastname: Rotstein
authors.affiliation: Partners MS Center, Brigham and Women's Hospital, Brookline, Massachusetts, United States of America.
authors.forename: Charles R G
authors.initials: CR
authors.lastname: Guttmann
authors.affiliation: Partners MS Center, Brigham and Women's Hospital, Brookline, Massachusetts, United States of America.
authors.forename: Rohit
authors.initials: R
authors.lastname: Bakshi
authors.affiliation: Partners MS Center, Brigham and Women's Hospital, Brookline, Massachusetts, United States of America.
authors.forename: Howard L
authors.initials: HL
authors.lastname: Weiner
authors.affiliation: College of Computer and Information Science, Northeastern, Boston, Massachusetts, United States of America.
authors.forename: Carla E
authors.initials: CE
authors.lastname: Brodley
authors.affiliation: Partners MS Center, Brigham and Women's Hospital, Brookline, Massachusetts, United States of America.
authors.forename: Tanuja
authors.initials: T
authors.lastname: Chitnis
mesh_terms: D000328:Adult; D001921:Brain / Q000000981:diagnostic imaging / Q000473:pathology; D018450:Disease Progression; D005260:Female; D006801:Humans; D016015:Logistic Models; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D008297:Male; D009103:Multiple Sclerosis / Q000473:pathology*; D059906:Neuroimaging; D012680:Sensitivity and Specificity; D060388:Support Vector Machine