StrokeRisk AI: Early-Warning Tool for Stroke Risk Prediction

Written By Amobi Andrew Onovo

Update (August 27, 2025):

We’re excited to announce that the StrokeRisk AI prototype officially launched on August 20, 2025. In just its first week, the tool has already recorded 400+ user sessions, reflecting strong interest in accessible, data-driven approaches to stroke prevention.

StrokeRisk AI is designed as an explainable, browser-based early-warning system that transforms routine demographic and clinical inputs into personalized stroke risk predictions in real time.

Why StrokeRisk AI?

Stroke remains the second leading cause of death worldwide and a critical public health challenge. While many risk factors are known, early and scalable screening tools are still limited—especially in low-resource settings.

StrokeRisk AI addresses this gap by providing a simple, fast, and interpretable risk assessment tool that can be used across clinical, research, and community health contexts.

Key Use Cases

  • Clinicians & Wellness Programs: Quickly flag high-risk individuals to prioritize preventive care.

  • Public Health Programs: Seamlessly integrate stroke screening into existing platforms for HIV, hypertension, and NCD management.

  • Researchers & Data Scientists: Investigate population-level patterns, validate models across diverse datasets, and adapt risk thresholds to different settings.

Who Should Use It?

  • Clinicians and health workers seeking practical, explainable risk flags in real time.

  • Public health managers integrating non-communicable disease (NCD) screening into broader programs.

  • Researchers and data scientists interested in validating and refining predictive models for different populations.

How It Works

  • Inputs: Age, BMI, smoking status, hypertension, heart disease, glucose level, and demographics.

  • Outputs:

    • A binary risk label (“At Risk” / “Not at Risk”).

    • A personalized probability score.

    • An explainability panel showing the top five contributing variables and their influence on prediction direction.

  • Threshold: The model uses a 0.38 probability cutoff, chosen to maximize sensitivity in screening settings. This prioritizes early detection—even if it means flagging some false positives—so fewer high-risk individuals are missed.

Next steps:

  • Partnering with health institutions and professionals to validate performance vs. routine screening.

  • Expanding inputs (blood pressure, nutrition, lifestyle, exercise).

  • Customizing model explainability and usability to reflect local health challenges, workflows, and user priorities in diverse community settings.

Explore the Prototype

👉 Watch the Demo Walkthrough
👉 Try StrokeRisk AI on Hugging Face

Amobi Andrew Onovo