Bridging Interpretability and Performance: Enhanced Machine Learning-Based Prediction of Hematoma Expansion Post-Stroke via Comprehensive Feature Selection

Bridging Interpretability and Performance: Enhanced Machine Learning-Based Prediction of Hematoma Expansion Post-Stroke via Comprehensive Feature Selection

Monitoring and controlling the occurrence of hematoma expansion events after a stroke is a primary clinical focus. The introduction of machine learning (ML) techniques offers intelligent decision support for physicians in this domain. However, for doctors without an ML background, the behavior of a...

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Bibliographic Details
Main Authors: Beigeng Zhao, Rui Song, Xu Guo, Lizhi Yu
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Hematoma expansion
post-stroke
machine learning
feature selection
model interpretability
Online Access:https://ieeexplore.ieee.org/document/10376172/

Internet

https://ieeexplore.ieee.org/document/10376172/

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