Efficacy and comparative performance of machine learning models for stroke risk prediction in hypertensive patients: A systematic review and meta-analysis

Abstract

Background: Stroke poses a significant health burden among hypertensive patients, where traditional risk models often lack precision. Machine learning (ML) has shown promise in enhancing prediction accuracy by integrating diverse data sources. Methods: , subgroup analyses, meta-regression, and leave-one-out sensitivity. The risk of bias was evaluated using PROBAST + AI, and the evidence quality was assessed using the GRADE approach. Results: = 76.8 %), potentially due to variations in study design and populations. Subgroup analyses showed consistent performance in Chinese studies (sensitivity 0.85, specificity 0.84) and those using multimodal features (sensitivity 0.84, specificity 0.83), with higher sensitivity for ischemic/hemorrhagic-specific models (0.90). Meta-regression explained 73.9 % of variance and No publication bias was detected (Deeks' p = 0.654). Conclusion: ML models demonstrate good performance for stroke prediction in hypertensive patients. However, heterogeneity underscores the need for standardized approaches. This evidence, rated moderate by GRADE, supports ML integration in clinical practice for improved prevention.

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