lorenzodenisi/Heart-Failure-Clinical-Records

Analisys of the dataset Heart Failures clinical records from UCI using different rebalancing techiniques and different models

27
/ 100
Experimental

This project helps medical researchers and data scientists predict patient survival after heart failure using clinical records. It takes patient data like age, blood pressure, and other clinical measurements, applies various data balancing and machine learning techniques, and outputs predictions about whether a patient will survive. This is designed for professionals in healthcare analytics or medical research.

No commits in the last 6 months.

Use this if you need to analyze heart failure patient data to identify survival patterns and risk factors using machine learning models.

Not ideal if you are a clinician looking for real-time diagnostic tools or personalized treatment recommendations.

cardiovascular-health patient-prognosis medical-research clinical-data-analysis survival-prediction
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 8 / 25
Community 14 / 25

How are scores calculated?

Stars

12

Forks

3

Language

Jupyter Notebook

License

Last pushed

Sep 14, 2020

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/lorenzodenisi/Heart-Failure-Clinical-Records"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.