Predicting stroke severity of patients using interpretable machine learning algorithms.

ABSTRACT:

Background

Stroke is a significant global health concern, ranking as the second leading cause of death and placing a substantial financial burden on healthcare systems, particularly in low- and middle-income countries. Timely evaluation of stroke severity is crucial for predicting clinical outcomes, with standard assessment tools being the Rapid Arterial Occlusion Evaluation (RACE) and the National Institutes of Health Stroke Scale (NIHSS). This study aims to utilize Machine Learning (ML) algorithms to predict stroke severity using these two distinct scales.

Methods

We conducted this study using two datasets collected from hospitals in Urmia, Iran, corresponding to stroke severity assessments based on RACE and NIHSS. Seven ML algorithms were applied, including K-Nearest Neig

SUBMITTER: Sorayaie Azar A 

PROVIDER: S-EPMC11562860 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature