Application of machine learning in migraine classification: a call for study design standardization and global collaboration.

ABSTRACT: Migraine is a complex neurological disorder with diverse clinical phenotypes and a multifaceted pathophysiology, which poses substantial challenges for accurate diagnosis, subtype differentiation, and biomarker discovery. Machine learning (ML) techniques have emerged as promising tools for classifying migraine patients and uncovering the underlying neurobiological mechanisms that differentiate migraine types and subtypes. This systematic review identifies current ML classification models for migraine types and subtypes, evaluating the quality, reproducibility, and clinical utility of published studies. The findings demonstrate that current ML models, particularly support vector machines and linear discriminant analysis, can accurately classify migraine patients based on structural and func

SUBMITTER: Petrusic I 

PROVIDER: S-EPMC12492770 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature