Machine Learning Analysis Applied to Prediction of Early Progression Independent of Relapse Activity in Multiple Sclerosis Patients.

ABSTRACT:

Background

Predicting prognosis in people with multiple sclerosis (pwMS) at early disease stages still remains an unmet need. Machine learning (ML) strategies demonstrated good reliability when applied for prediction in medicine. This study aimed at developing a predictive algorithm comparing different ML approaches, by using routine demographic, clinical and radiological data from a large multicentric cohort of newly diagnosed pwMS.

Methods

Demographic, clinical, radiological and biochemical data were retrospectively collected at three Italian MS centers at baseline and four timepoints thereafter (6, 12, 24, and 36 months). Data from the first evaluation and subsequent 2-year follow-up were analyzed, comparing different ML models (Random Forest, Extra Trees, XGBoost, Logist

SUBMITTER: Poretto V 

PROVIDER: S-EPMC12661373 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature