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Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

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Published on: January 15, 2017

The most efficient machine learning algorithms in stroke prediction: A systematic review.

Farkhondeh Asadi¹, Milad Rahimi², Amir Hossein Daeechini¹

¹Department of Health Information Technology and Management School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences Tehran Iran.

Health Science Reports

|October 2, 2024

Summary

This summary is machine-generated.

Machine learning algorithms show promise for stroke prediction, with Random Forest being highly efficient. Continued research is needed to improve accuracy and reliability across diverse datasets.

Keywords:

artificial intelligence machine learning prediction stroke

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Area of Science:

Medical Informatics
Computational Neuroscience
Public Health

Background:

Stroke is a leading global cause of mortality and disability.
Effective stroke prediction is crucial for mitigating its severe impact on quality of life.

Purpose of the Study:

To systematically review machine learning algorithms for stroke prediction.
To identify and compare the most efficient algorithms published between 2019 and August 2023.

Main Methods:

Systematic literature search across PubMed, Scopus, Web of Science, and IEEE.
Keywords included

Main Results:

Twenty articles were analyzed, identifying Random Forest (RF) as the most efficient algorithm in 25% of studies.
Other effective algorithms include Support Vector Machines (SVM), XGBOOST, and Artificial Neural Networks (ANN).

Conclusions:

Machine learning for stroke prediction has advanced rapidly, with notable accuracy improvements.
No current model achieves perfect accuracy; variations in datasets and sample sizes affect performance.
Future research should standardize datasets and sample sizes for more reliable stroke prediction models.