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](https://archive.ics.uci.edu/ml/datasets/diabetes+130-us+hospitals+for+years+1999-2008)). See details there and use carefully.

Basic preprocessing done by the [imodels team](https://github.com/csinva/imodels) in [this notebook](https://github.com/csinva/imodels-data/blob/master/notebooks_fetch_data/00_get_datasets_custom.ipynb).

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=['readmitted'])
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['readmitted'].values
print('accuracy', np.mean(m.predict(X_test) == y_test))
```

---
annotations_creators: []
language: []
language_creators: []
license: []
multilinguality: []
pretty_name: 糖尿病再入院（diabetes-readmission）
size_categories:
- 10万 < 样本量 < 100万
source_datasets: []
tags:
- 可解释性（interpretability）
- 公平性（fairness）
- 医学（medicine）
task_categories:
- 表格分类（tabular-classification）
task_ids: []
---

本数据集为从UCI（University of California, Irvine）移植的糖尿病再入院（diabetes-readmission）数据集，原始链接见[此处](https://archive.ics.uci.edu/ml/datasets/diabetes+130-us+hospitals+for+years+1999-2008)。请参考原始文档并谨慎使用。

基础预处理工作由[imodels团队](https://github.com/csinva/imodels)在[该代码笔记本](https://github.com/csinva/imodels-data/blob/master/notebooks_fetch_data/00_get_datasets_custom.ipynb)中完成。

任务目标为二分类结果`再入院（readmitted）`。

### 示例用法

#### 加载数据

from datasets import load_dataset

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

#### 拟合模型

import imodels
import numpy as np

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

#### 评估

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

原始信息汇总

数据集概述

数据集名称

• 名称：diabetes-readmission

数据集属性

• 大小：100K<n<1M
• 标签：
  • interpretability
  • fairness
  • medicine
• 任务类别：tabular-classification

数据集目标

• 目标变量：readmitted（二元分类）

数据集使用示例

• 加载数据： python from datasets import load_dataset dataset = load_dataset("imodels/diabetes-readmission") df = pd.DataFrame(dataset[train]) X = df.drop(columns=[readmitted]) y = df[readmitted].values

• 模型训练： python import imodels import numpy as np m = imodels.FIGSClassifier(max_rules=5) m.fit(X, y) print(m)

• 模型评估： python df_test = pd.DataFrame(dataset[test]) X_test = df.drop(columns=[readmitted]) y_test = df[readmitted].values print(accuracy, np.mean(m.predict(X_test) == y_test))