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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
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.
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.
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