CardioVision3D

CardioVision3D uses machine learning, explainable AI, and 3D heart visualization to explore cardiovascular risk and interpret AI predictions for research and education.

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Inspiration

Cardiovascular diseases are a major health concern. I wanted to explore how AI could help analyze cardiovascular data and make machine learning predictions easier to understand through interactive visualization.

What It Does

CardioVision3D combines machine learning, explainable AI, and interactive 3D heart visualization. It explores cardiovascular indicators and supports predictions for Cath, LAD, LCX, and RCA using clinical and laboratory features from the Z-Alizadeh Sani dataset.

How We Built It

The project uses Python, pandas, NumPy, scikit-learn, Streamlit, and Plotly. We developed data preprocessing and model training pipelines, evaluated predictions using performance metrics, and created an interactive schematic of the heart. SHAP and LIME can provide feature-level explanations when integrated into the prediction workflow.

Challenges

Key challenges included preprocessing clinical data, preventing target leakage, handling different prediction targets, evaluating model performance, and connecting model outputs with clear visualizations.

What I Learned

This project helped me understand machine learning workflows, clinical data analysis, model evaluation, explainable AI concepts, and interactive application development. It also reinforced the importance of transparency, responsible AI, and honest reporting of model limitations.

Future Scope

Future improvements include stronger model validation, enhanced explainability, better 3D visualization, and secure deployment.

Disclaimer: CardioVision3D is an experimental educational and research project, not a clinically validated diagnostic tool. Its predictions should not replace professional medical advice.

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