Fed-Heart Disease

The Heart Disease dataset [1] was collected in 1988 in four centers: Cleveland, Hungary, Switzerland and Long Beach V. We do not own the copyright of the data: everyone using this dataset should abide by its licence and give proper attribution to the original authors. It is available for download here.

Dataset description

Please refer to the dataset website for an exhaustive data sheet. The table below provides a high-level description of the dataset.

	Dataset description
Description	Heart Disease dataset.
Dataset size	39,6 KB.
Centers	4 centers - Cleveland, Hungary, Switzerland and Long Beach V.
Records per center	Train/Test: 199/104, 172/89, 30/16, 85/45.
Inputs shape	16 features (tabular data).
Total nb of points
Task	Binary classification

License and data usage terms

This dataset is licensed under a Creative Commons Attribution 4.0 International (CC-BY 4.0) license by its authors. Anyone using this dataset should abide by its licence and give proper attribution to the original authors.

Ethics

As per the dataset website, sensitive entries of the dataset were removed by the original authors:

The names and social security numbers of the patients were recently removed from the database, replaced with dummy values.

Download and preprocessing instructions

To download the data, First cd into the dataset_creation_scripts folder:

cd flamby/datasets/fed_heart_disease/dataset_creation_scripts

then simply run the following command:

python download.py --output-folder ./heart_disease_dataset

This will download 38.6ko of data.

IMPORTANT : If you choose to relocate the dataset after downloading it, it is imperative that you run the following script otherwise all subsequent scripts will not find it:

python update_config.py --new-path /new/path/towards/dataset

Using the dataset

Now that the dataset is ready for use you can load it using the low or high-level API by doing:

from flamby.datasets.fed_heart_disease import FedHeartDisease, HeartDiseaseRaw

# To load the first center as a pytorch dataset
center0 = FedHeartDisease(center=0, train=True)
# To load the second center as a pytorch dataset
center1 = FedHeartDisease(center=1, train=True)
# To sample batches from each of the local datasets use the traditional pytorch API
from torch.utils.data import DataLoader as dl

X, y = iter(dl(center0, batch_size=16, shuffle=True, num_workers=0)).next()

More informations on how to train model and handle flamby datasets in general are available in the Quickstart

Benchmarking the baseline on a pooled setting

In order to benchmark the baseline on the pooled dataset one need to download and preprocess the dataset and launch the following script:

python benchmark.py

This will train a logistic regression classifier (which is the strongest baseline according to UCI ML Repository.

References

[1] Janosi, Andras, Steinbrunn, William, Pfisterer, Matthias, Detrano, Robert & M.D., M.D.. (1988). Heart Disease. UCI Machine Learning Repository.