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🧠 How Machine Learning Predicts Stroke Risk

A 3D risk-landscape built from a simplified gradient-boosted model, showing how age, glucose, blood pressure and lifestyle factors combine into a single stroke-risk score.

AI & Machine Learning3DAdvanced60 FPS

A 3D risk landscape built from a simplified gradient-boosted model, showing how age, glucose, blood pressure and lifestyle factors combine into a single stroke-risk score, the same way an XGBoost-style clinical model reasons.

🔬 What It Demonstrates

The terrain's height at any age/glucose combination is the model's predicted risk; the contribution bars break that single number back down into what each feature contributed, and the tree ensemble shows how many "votes" pushed toward elevated risk.

🎮 How to Use

Move the age, glucose and systolic BP sliders and toggle smoking or hypertension. Watch the patient marker climb or descend the risk landscape, the bars swing red or blue, and the dial's needle sweep live.

💡 Did You Know?

Boosted-tree models are popular for tabular clinical data precisely because they capture this kind of non-linear, interacting risk surface without needing hand-engineered feature crosses.

⚙ Under the hood

A 3D risk-landscape built from a simplified gradient-boosted model, showing how age, glucose, blood pressure and lifestyle factors combine into a single stroke-risk score.

machine learningstroke predictionrisk assessmentgradient boostingmedical diagnosisartificial intelligenceThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install