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Machine learning to predict stroke risk from routine hospital data: A systematic review.

William Heseltine-Carp¹, Megan Courtman², Daniel Browning¹

¹University of Plymouth, Room N6, ITTC Building, Plymouth Science Park, Plymouth PL68BX, UK.

International Journal of Medical Informatics

|February 5, 2025

Summary

Machine learning (ML) shows promise for predicting stroke risk using hospital data, outperforming traditional methods. However, further research is needed to improve model accuracy and clinical integration.

Keywords:

Artificial intelligence Ischaemic stroke Machine learning Risk evaluation Routine hospital data Stroke

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Area of Science:

Cardiology
Medical Informatics
Data Science

Background:

Stroke is a major cause of death and disability.
Current stroke risk prediction tools have limited accuracy, especially for non-atrial fibrillation patients.
There is a need for improved stroke risk stratification models.

Purpose of the Study:

To systematically review research on machine learning (ML) for stroke risk prediction using routine hospital data.
To identify methodological limitations and provide recommendations for future ML-based stroke prediction research.

Main Methods:

A systematic review of 49 original research articles published between January 2013 and December 2024.
Studies utilized machine learning algorithms and routine hospital data to predict stroke risk.
Searches were conducted in the PUBMED database, including general and atrial fibrillation-specific populations.

Main Results:

Machine learning models demonstrated high accuracy (AUCs 0.64-0.99) in predicting stroke risk.
ML models consistently outperformed traditional tools like CHA 2 DS 2 -VASc.
ML identified novel risk factors from ECG, lab, and echocardiography data, but dataset quality, overfitting, and lack of validation were noted.

Conclusions:

Machine learning holds significant potential for stroke risk prediction and novel risk factor identification.
Improvements in study methodology, including adherence to EQUATOR guidelines and interdisciplinary collaboration, are crucial.
Prospective studies are needed to validate ML models and assess barriers to clinical integration.