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Ischemic Stroke l: Introduction

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Ischemic stroke is an acute cerebrovascular condition in which blood flow to a brain region is suddenly interrupted, leading to tissue infarction. Neurons depend on continuous oxygen and glucose supply, so even brief reductions in perfusion cause energy failure, ionic imbalance, and irreversible injury. Ischemic strokes are classified into thrombotic and embolic types based on their underlying mechanisms.Thrombotic MechanismsThrombotic stroke develops when a clot forms within a cerebral artery.

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Updated: Jun 6, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Explainable and Interpretable Model for the Early Detection of Brain Stroke Using Optimized Boosting Algorithms.

Yogita Dubey¹, Yashraj Tarte¹, Nikhil Talatule¹

¹Department of Electronics and Telecommunication, Yeshwantrao Chavan College of Engineering, Nagpur 441110, India.

Diagnostics (Basel, Switzerland)

|November 27, 2024

Summary

This summary is machine-generated.

Machine learning accurately predicts stroke patient survival using demographic, medical, and lifestyle data. XGBoost achieved the highest accuracy, offering insights for personalized stroke treatment strategies.

Keywords:

LIME SHAP explainable AI machine learning stroke prediction

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Public Health

Background:

Stroke is a leading cause of death and disability worldwide.
Cerebral blood flow interruption leads to irreversible brain damage.
Predictive modeling can aid in managing stroke patient outcomes.

Purpose of the Study:

To develop a machine learning model for predicting stroke patient survival.
To identify key factors influencing stroke patient survival.
To provide insights for personalized stroke treatment.

Main Methods:

Utilized a random sampling method for imbalanced stroke data.
Employed optimized boosting machine learning algorithms (Gradient Boosting, AdaBoost, XGBoost).
Integrated explainable AI (LIME, SHAP) for model interpretability.

Main Results:

XGBoost demonstrated superior performance in stroke prediction.
Achieved 96.97% training accuracy and 92.13% testing accuracy with XGBoost.
Identified significant correlations between features and patient survival.

Conclusions:

Machine learning models can effectively predict stroke patient survival.
XGBoost offers a robust approach for stroke prediction.
Findings can inform healthcare practitioners in developing personalized treatment plans.