Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis.

ABSTRACT:

Background

Robust predictive models of clinical impairment and worsening in multiple sclerosis (MS) are needed to identify patients at risk and optimize treatment strategies.

Objective

To evaluate whether machine learning (ML) methods can classify clinical impairment and predict worsening in people with MS (pwMS) and, if so, which combination of clinical and magnetic resonance imaging (MRI) features and ML algorithm is optimal.

Methods

We used baseline clinical and structural MRI data from two MS cohorts (Berlin: n = 125, Amsterdam: n = 330) to evaluate the capability of five ML models in classifying clinical impairment at baseline and predicting future clinical worsening over a follow-up of 2 and 5 years. Clinical worsening was defined by increases in the Expanded Di

SUBMITTER: Noteboom S 

PROVIDER: S-EPMC11319410 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature