Datasets:

imodels
/

diabetes-readmission

annotations_creators: []
language: []
language_creators: []
license: []
multilinguality: []
pretty_name: diabetes-readmission
size_categories:
  - 100K<n<1M
source_datasets: []
tags:
  - interpretability
  - fairness
  - medicine
task_categories:
  - tabular-classification
task_ids: []

Port of the diabetes-readmission dataset from UCI (link here). See details there and use carefully.

Basic preprocessing done by the imodels team in this notebook.

The target is the binary outcome readmitted.

Sample usage

Load the data:

from datasets import load_dataset

dataset = load_dataset("imodels/diabetes-readmission")
df = pd.DataFrame(dataset['train'])
X = df.drop(columns=['is_recid'])
y = df['readmitted'].values

Fit a model:

import imodels
import numpy as np

m = imodels.FIGSClassifier(max_rules=5)
m.fit(X, y)
print(m)

Evaluate:

df_test = pd.DataFrame(dataset['test'])
X_test = df.drop(columns=['readmitted'])
y_test = df['is_recid'].values
print('accuracy', np.mean(m.predict(X_test) == y_test))