Predictive etiological classification of acute ischemic stroke through interpretable machine learning algorithms: a multicenter, prospective cohort study.

ABSTRACT:

Background

The prognosis, recurrence rates, and secondary prevention strategies varied significantly among different subtypes of acute ischemic stroke (AIS). Machine learning (ML) techniques can uncover intricate, non-linear relationships within medical data, enabling the identification of factors associated with etiological classification. However, there is currently a lack of research utilizing ML algorithms for predicting AIS etiology.

Objective

We aimed to use interpretable ML algorithms to develop AIS etiology prediction models, identify critical factors in etiology classification, and enhance existing clinical categorization.

Methods

This study involved patients with the Third China National Stroke Registry (CNSR-III). Nine models, which included Natural Gradien

SUBMITTER: Chen S 

PROVIDER: S-EPMC11384709 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature