Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression.

ABSTRACT:

Background

Ischemic Stroke (IS) stands as a leading cause of mortality and disability globally, with an anticipated increase in IS-related fatalities by 2030. Despite therapeutic advancements, many patients still lack effective interventions, underscoring the need for improved prognostic assessment tools. Machine Learning (ML) models have emerged as promising tools for predicting stroke prognosis, surpassing traditional methods in accuracy and speed.

Objective

The aim of this study was to develop and validate ML algorithms for predicting the 6-month prognosis of patients with Acute Cerebral Infarction, using clinical data from two medical centers in China, and to assess the feasibility of implementing Explainable ML in clinical settings.

Methods

A retrospective observ

SUBMITTER: Guo K 

PROVIDER: S-EPMC11913711 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature