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README.md
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# Dataset Card for
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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.
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## Maintenance
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```bash
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git clone git@hf.co:datasets/MuGemSt/Pima
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cd Pima
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```
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## Usage
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```python
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from datasets import load_dataset
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dataset = load_dataset("
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for item in dataset["train"]:
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print(item)
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```
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## Mirror
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<https://www.modelscope.cn/datasets/
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## Reference
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[1] [Pima Indians Diabetes Database](https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database)<br>
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[2] [Chapter IV ‐ Medical Signal Segmentation and Classification](https://github.com/
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# Dataset Card for Pima
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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.
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## Usage
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```python
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from datasets import load_dataset
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dataset = load_dataset("Genius-Society/Pima")
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for item in dataset["train"]:
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print(item)
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```
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## Mirror
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<https://www.modelscope.cn/datasets/Genius-Society/Pima>
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## Reference
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[1] [Pima Indians Diabetes Database](https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database)<br>
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[2] [Chapter IV ‐ Medical Signal Segmentation and Classification](https://github.com/Genius-Society/Medical_Image_Computing/wiki/Chapter-IV-%E2%80%90-Medical-Signal-Segmentation-and-Classification)
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