Machine learning techniques for stroke prediction: A systematic review of algorithms, datasets, and regional gaps.
Authors
Affiliations (5)
Affiliations (5)
- Department of Computer Engineering, Federal University Oye-Ekiti, Ekiti, Nigeria.
- Department of Medicine, Ladoke Akintola University of Technology, Ogbomoso, Nigeria.
- Stroke Unit, Mid and South Essex NHS Foundation Trust, Westcliff-On-Sea SS0 0RY Northern Ireland, United Kingdom.
- Coltishall Medical Practice, NHS GP Surgery, Norfolk NR12 7HA, United Kingdom; Department of Primary Care, University of East Anglia, Norwich, United Kingdom.
- Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, Northern Ireland, United Kingdom; Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY Northern Ireland, United Kingdom; Department of Public Health, York St John University, London, Northern Ireland, United Kingdom. Electronic address: [email protected].
Abstract
Stroke is a leading cause of mortality and disability worldwide, with approximately 15 million people suffering strokes annually. Machine learning (ML) techniques have emerged as powerful tools for stroke prediction, enabling early identification of risk factors through data-driven approaches. However, the clinical utility and performance characteristics of these approaches require systematic evaluation. To systematically review and analyze ML techniques used for stroke prediction, systematically synthesize performance metrics across different prediction targets and data sources, evaluate their clinical applicability, and identify research trends focusing on patient population characteristics and stroke prevalence patterns. A systematic review was conducted following PRISMA guidelines. Five databases (Google Scholar, Lens, PubMed, ResearchGate, and Semantic Scholar) were searched for open-access publications on ML-based stroke prediction published between January 2013 and December 2024. Data were extracted on publication characteristics, datasets, ML methodologies, evaluation metrics, prediction targets (stroke occurrence vs. outcomes), data sources (EHR, imaging, biosignals), patient demographics, and stroke prevalence. Descriptive synthesis was performed due to substantial heterogeneity precluding quantitative meta-analysis. Fifty-eight studies were included, with peak publication output in 2021 (21 articles). Studies targeted three main prediction objectives: stroke occurrence prediction (n = 52, 62.7 %), stroke outcome prediction (n = 19, 22.9 %), and stroke type classification (n = 12, 14.4 %). Data sources included electronic health records (n = 48, 57.8 %), medical imaging (n = 21, 25.3 %), and biosignals (n = 14, 16.9 %). Systematic analysis revealed ensemble methods consistently achieved highest accuracies for stroke occurrence prediction (range: 90.4-97.8 %), while deep learning excelled in imaging-based applications. African populations, despite highest stroke mortality rates globally, were represented in fewer than 4 studies. ML techniques show promising results for stroke prediction. However, significant gaps exist in representation of high-risk populations and real-world clinical validation. Future research should prioritize population-specific model development and clinical implementation frameworks.