# Predictive performance of machine learning models in acute ischemic stroke: a systematic review and meta-analysis

## TL;DR

This study reviews and compares machine learning models for predicting outcomes in acute ischemic stroke, finding strong performance but significant variability.

## Contribution

The study provides a systematic review and meta-analysis of ML models for acute ischemic stroke prognosis, comparing algorithm effectiveness.

## Key findings

- ML models showed a pooled AUC of 0.87 for predicting acute ischemic stroke outcomes.
- Random forest and SVM outperformed logistic regression in predictive accuracy.
- High heterogeneity limits generalizability of model performance estimates.

## Abstract

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.

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 analyse the data and assessed heterogeneity (I2) statistic using SPSS-29 and R-Studio-4.2.0.

A total of 14 studies were included in the systematic review, with 12 eligible for meta-analysis. The pooled AUC of ML models was 0.87 (95% CI, 0.83–0.91), demonstrating strong predictive performance despite substantial heterogeneity (I2 = 99%). Random forest (RF) (AUC = 0.85) and SVM (AUC = 0.82) outperformed logistic regression (LR) (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).

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.

https://www.crd.york.ac.uk/PROSPERO/view/CRD420251033217, (Registration number: CRD420251033217).