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 misclas

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC5381810 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature