SYSTEMATIC REVIEW article
Front. Neurol.
Sec. Stroke
Predictive Performance of Machine Learning Models in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis
Uzma Khanum 1
Vasudeva Guddattu 2
Shasthara Paneyala 1
Asha Srinivasan 1
Chaithra N 1
1. JSS Academy of Higher Education and Research, Mysore, India
2. Manipal academy of higher education, Udupi, India
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Abstract
Introduction Acute Ischemic Stroke (AIS) is a leading cause of global mortality and disability worldwide. Machine Learning (ML) models enhance prognostic accuracy by analysing complex, multidimensional clinical data. The aim of this systematic review and meta-analysis is to identify the gaps in the current ML models, along with methodological and performance outcomes in AIS. Further, the study objective was to identify the most frequently used algorithms and compare their relative effectiveness, thereby supporting future research to develop novel ML-based predictive models for stroke care management. Methods The systematic review followed PRISMA guidelines with PROSPERO registration. A comprehensive search was performed in PubMed, Scopus, and Web of Science using MeSH keywords. Data extraction captured study characteristics, ML algorithms, and outcome metrics. We used the PROBAST and TRIPOD-AI to assess the qualities and bias of included studies. Meta-analysis of AUC values across ML models were conducted to synthesize model performance used a random-effects model to summarize and analyze the data and assessed heterogeneity (I²) statistic using SPSS -29 and R-Studio- 4.2.0.ResultsA total of 14 studies were included in the systematic review, with 12 eligible for meta-analysis. The pooled AUC of machine learning models was 0.87 (95% CI: 0.83–0.91), demonstrating strong predictive performance despite substantial heterogeneity (I² = 99%). Random Forest (AUC = 0.85) and SVM (AUC = 0.82) outperformed Logistic Regression (AUC = 0.75), while XGBoost showed stable performance (AUC = 0.82); heterogeneity was mainly driven by study design, publication year, and algorithm type (p < 0.001). Conclusion -ML-based models show potential for improving prognostic assessment in AIS; however, substantial heterogeneity and methodological limitations across studies limit the generalizability of pooled performance estimates.
Summary
Keywords
Acute ischemic stroke, machine learning, Meta-analysis, outcome prediction, prognosis
Received
19 December 2025
Accepted
19 February 2026
Copyright
© 2026 Khanum, Guddattu, Paneyala, Srinivasan and N. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Chaithra N
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