StrokeRisk prediction: an AI-powered early assessment tool

MSCA Data Consulting partnered with a healthcare-focused client to design and build an AI-powered clinical decision support tool for early stroke risk assessment. The solution combines machine learning with explainable AI to give medical professionals transparent, point-of-care insights they can trust and act on with confidence.

Building a reliable stroke prediction tool meant tackling a severe class imbalance — only 4.9% of records were positive stroke cases. The dataset also had missing clinical records, adding further difficulty to accurate modeling.

Beyond the data itself, medical professionals needed to trust the tool's outputs. A “black-box” AI system with unexplained predictions wasn't going to be adopted in a real clinical setting.

MSCA built a Streamlit web application powered by Python and SQLite, giving the tool a lightweight, responsive interface suited for point-of-care use.

The team integrated hybrid supervised and unsupervised models — XGBoost, Random Forests, and K-Means — to handle the class imbalance, and embedded SHAP to explain every risk assessment the system produced.

The result was a transparent, point-of-care clinical tool delivering verifiable risk evaluations with clear, feature-level explanations behind every prediction.

This transparency directly boosted clinician trust in the tool, while built-in patient record CRUD functionality streamlined day-to-day workflows for medical staff.

As healthcare organizations continue to look for AI tools they can actually trust, explainable, point-of-care solutions like this one position MSCA as a long-term partner in bringing responsible AI into clinical settings — not just building models, but building systems clinicians will use.