Heart Attack Predictor

The Challenge

Early detection of heart attack risk can save lives, but clinical risk assessment tools are often complex and inaccessible.

Approach & Solution

Trained and compared multiple ML models (Random Forest, XGBoost, Logistic Regression) on clinical datasets. Built a Flask web interface for instant risk assessment.

Results & Impact

• Achieved 92% prediction accuracy on test dataset
• Compared 3+ model architectures for optimal performance
• User-friendly web interface for clinical data input
• Instant risk score with explanation of contributing factors