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July 14, 2025International Journal of Medical InformaticsOpen Access

Machine learning techniques for stroke prediction: A systematic review of algorithms, datasets, and regional gaps

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Authors

ASAfeez A. SoladoyeNANicholas AderintoMPMayowa Racheal Popoola

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Overview

Systematic review analyzes machine learning techniques for stroke prediction, highlighting regional gaps and data sources.

Key Points

  • Machine learning techniques provide valuable tools for stroke prediction, showing potential for early risk identification.
  • Ensemble methods consistently achieved the highest accuracy rates for predicting stroke occurrence, ranging from 90.4% to 97.8%.
  • The review analyzed 58 studies, focusing on various prediction objectives, data sources, and patient demographics.
  • Significant gaps exist in the representation of high-risk populations, especially in African communities with high stroke mortality rates.

Cite This Study

Soladoye et al. (2025) studied this question.

synapsesocial.com/papers/689a02b6e6551bb0af8cc288https://doi.org/10.1016/j.ijmedinf.2025.106041
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