Description

This dataset was created by combining different datasets already available independently but not combined before. In this dataset, 5 heart datasets are combined over 11 common features. Every dataset used can be found under the Index of heart disease datasets from UCI Machine Learning Repository on the following link: https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/.

Version

Version 1

Sensitive Data

Sensitive Data Used: ['Sex', 'Age']
Justification: Demographic information like age and gender often have a strong correlation with health outcomes. For example, older patients are more likely to have a higher risk of heart disease.

Reference

https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction

Citation

@misc{fedesoriano, title={Heart Failure Prediction Dataset.}, author={Fedesoriano, F}, year={2021}, publisher={Kaggle} }

License

Identifier: CC0-1.0

Graphics

Graphics

Description

The model was trained on the Kaggle Heart Failure Prediction Dataset to predict risk of heart failure.

Version

Date: 2024-07-16
Initial Release
Version: 0.0.1

Owners

Name: CyclOps Team
Contact: vectorinstitute.github.io/cyclops/
Email: cyclops@vectorinstitute.ai

Licenses

Identifier: Apache-2.0

References

https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html

Name

Heart Failure Prediction Model

Users

Hospitals

Clinicians

ML Engineers

Use Cases

Predicting risk of heart failure.
Kind: primary

Predicting risk of pathologies and conditions other than heart failure.
Kind: out-of-scope

Fairness Assessment

Affected Group: sex, age
Benefits: Improved health outcomes for patients.
Harms: Biased predictions for patients in certain groups (e.g. older patients) may lead to worse health outcomes.
We will monitor the performance of the model on these groups and retrain the model if the performance drops below a certain threshold.

Ethical Considerations

The model should be continuously monitored for performance and retrained if the performance drops below a certain threshold.
Risk: The model may be used to make decisions that affect the health of patients.