This application uses a trained machine learning model to estimate the probability of cardiovascular disease based on clinical metrics. By analyzing historical patient data, the algorithm identifies complex patterns across multiple risk factors to provide a rapid, data-driven health assessment.
297
Patients
13
Features
80.5%
Accuracy
Data: UCI Heart Disease Dataset, Cleveland Clinic 1988
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Built with Python, scikit-learn, Flask
For educational and informational purposes only. This tool does not provide medical advice, diagnosis, or treatment.