File size: 1,915 Bytes
7a36620 75d872a f4900b4 75d872a 7a36620 900e775 e061ee1 5b94188 900e775 7e765be 63244bc 7e765be 900e775 7e765be 6280d17 900e775 6280d17 900e775 6280d17 900e775 ba25917 6280d17 651ba69 6280d17 ba25917 8c6c3d8 7e765be b2fbc73 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |
---
license: mit
task_categories:
- feature-extraction
- token-classification
language:
- en
tags:
- biology
- medical
pretty_name: Pima
size_categories:
- n<1K
---
# Dataset Card for "MuGeminorum/Pima"
The Pima dataset is a well-known data repository in the field of healthcare and machine learning. The dataset contains demographic, clinical and diagnostic characteristics of Pima Indian women and is primarily used to predict the onset of diabetes based on these attributes. Each data point includes information such as age, number of pregnancies, body mass index, blood pressure, and glucose concentration. Researchers and data scientists use the Pima dataset to develop and evaluate predictive models for diabetes risk assessment. The dataset plays a key role in driving the development of machine learning algorithms aimed at improving the early detection and management of diabetes. Its relevance is not limited to clinical applications, but extends to research initiatives focusing on factors that influence the prevalence of diabetes. The Pima dataset becomes a cornerstone in fostering innovation in predictive healthcare analytics, contributing to the broad field of medical informatics.
## Maintenance
```bash
git clone git@hf.co:datasets/MuGeminorum/Pima
cd Pima
```
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("MuGeminorum/Pima")
for item in dataset["train"]:
print(item)
for item in dataset["validation"]:
print(item)
for item in dataset["test"]:
print(item)
```
## Mirror
<https://www.modelscope.cn/datasets/MuGeminorum/Pima>
## Reference
[1] [Pima Indians Diabetes Database](https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database)<br>
[2] [Chapter IV ‐ Medical Signal Segmentation and Classification](https://github.com/MuGeminorum/Medical_Image_Computing/wiki/Chapter-IV-%E2%80%90-Medical-Signal-Segmentation-and-Classification) |