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dashboard
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Overall CAD Prediction
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Coronary Vessel Predictions
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Vessel Comparison
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Interactive 3D Heart Viewer
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IT says Influences the Model
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patient enters its detail
Inspiration
CardioVision AI: Cardiovascular Risk Visualization & Prediction
Cardiovascular diseases are among the leading causes of death worldwide, making early risk assessment and better understanding of coronary artery disease (CAD) increasingly important. However, interpreting clinical data and understanding potential coronary vessel involvement can be challenging.
CardioVision AI is a machine learning-powered web application designed to make cardiovascular risk analysis more accessible, interpretable, and data-driven. Using clinical features from the Z-Alizadeh Sani dataset, including patient demographics, vital signs, laboratory measurements, and clinical findings, the system analyzes patient information to predict the likelihood of coronary artery disease.
The platform integrates a trained machine learning model with an interactive dashboard that presents predictions, evaluation metrics, and insights into influential clinical features. It aims to help users explore the relationship between patient characteristics and predicted cardiovascular risk through clear visualizations and interpretable outputs. The architecture is designed to support future integration of vessel-specific predictions for the left anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA), alongside interactive three-dimensional coronary anatomy.
By combining machine learning, explainability, and visualization, CardioVision AI demonstrates how complex clinical data can be transformed into understandable insights. Our vision is to support cardiovascular research and clinical decision-support development through transparent, accessible technology. This prototype is intended for research and educational use, not as a replacement for professional medical diagnosis or clinical judgment.
