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Hemorrhagic Stroke l: Introduction

Hemorrhagic Stroke l: Introduction

A hemorrhagic stroke is an acute neurological event that occurs when a weakened cerebral blood vessel ruptures, allowing blood to accumulate within or around the brain. The sudden release of blood forms a focal hematoma that increases intracranial pressure, displaces neural tissue, and can obstruct cerebrospinal fluid pathways. These effects may be compounded by intraventricular extension of the hemorrhage, cerebral edema, or compression of adjacent structures, all of which contribute to...

Ischemic Stroke l: Introduction

Ischemic Stroke l: Introduction

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.

Hemorrhagic Stroke ll: Pathophysiology

Hemorrhagic Stroke ll: Pathophysiology

A hemorrhagic stroke develops when a cerebral blood vessel ruptures, allowing blood to escape into the surrounding brain tissue, as in intracerebral hemorrhage (ICH), or into the subarachnoid space, as in subarachnoid hemorrhage (SAH). Because the skull is a rigid compartment, the sudden presence of extravascular blood rapidly increases intracranial pressure and compresses adjacent neural structures, leading to immediate tissue injury and impaired cerebral perfusion.Mass Effect and Primary...

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Related Experiment Video

Updated: Apr 28, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

A hybrid random forest model for stroke risk prediction.

Qixuan Lu¹, Shuo Li¹, Hongyuan Xu¹

¹Department of Cerebral Vascular Disease, Neurological Disease Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.

Frontiers in Neurology

|April 27, 2026

Summary

A new random forest model accurately predicts stroke risk using accessible data, outperforming existing methods. This tool can aid clinical decisions and guideline application.

Keywords:

machine learning prediction model random forest risk stratification stroke

Related Experiment Videos

Last Updated: Apr 28, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Medical Informatics
Machine Learning in Healthcare
Cardiovascular Disease Research

Background:

Stroke remains a leading cause of disability and mortality worldwide.
Accurate stroke risk prediction is crucial for timely intervention and prevention strategies.
Existing prediction models may lack accuracy or generalizability in diverse populations.

Purpose of the Study:

To develop and optimize a machine learning-based stroke risk prediction tool.
To evaluate the performance of multiple machine learning algorithms for stroke risk prediction.
To create a model utilizing easily obtainable data for clinical practice.

Main Methods:

Retrospective analysis of a large, multicenter health database (35,859 participants).
Data preprocessing included outlier removal, missing value imputation, and addressing class imbalance using Synthetic Minority Over-sampling Technique.
Seven machine learning algorithms were fitted, with a focus on the random forest model.

Main Results:

The study included 781 stroke events (2.2%) from 35,859 participants.
The random forest model exhibited superior performance, demonstrating high predictive value and discrimination.
The optimized random forest model achieved an Area Under the Curve (AUC) of 0.97 for stroke risk prediction.

Conclusions:

A novel random forest algorithm-based stroke risk prediction model was successfully developed.
The developed model outperformed established stroke risk prediction methods.
Further validation and optimization are recommended to enhance generalizability and clinical application for shared decision-making.