CardioGuard AI: Intelligent Heart Risk Prediction

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  Model Performance Comparison for Heart Disease Risk Prediction

Cardiovascular disease remains the leading cause of death worldwide, yet early detection is still inaccessible for many individuals. We were inspired by a simple but urgent question:

What if routine clinical data could be transformed into an intelligent early-warning system?

Millions of patients undergo standard medical tests—blood pressure, cholesterol levels, ECG readings, heart rate measurements—but this data is often used only for immediate diagnosis rather than predictive prevention. We wanted to leverage machine learning to move from reactive care to proactive risk assessment.

Hack4Health’s mission to democratize computational medicine deeply resonated with us. As students without access to advanced medical labs, we saw an opportunity to use real biomedical datasets and accessible tools to build something meaningful. Our goal was to create a system that not only predicts cardiovascular disease risk but also translates complex model outputs into understandable, actionable insights.

CardioGuard AI was born from the belief that AI should empower early intervention, support clinicians, and make preventive healthcare more accessible to everyone.

Built With

• accuracy
• colab
• confusion
• curve
• google
• gradientboostingclassifier
• jupyter
• logisticregression
• matplotlib
• matrix
• numpy
• pandas
• python
• randomforestclassifier
• roc
• roc-auc
• scikit-learn
• score
• seaborn
• split
• standardscaler
• train-test