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Synthetic Gestational Diabetes Dataset
Patient Health Records & Digital Health
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£179.99
About
The Synthetic Gestational Diabetes Dataset is designed for educational and research purposes to analyze health-related factors contributing to gestational diabetes risk. It provides anonymized, synthetic data on individuals’ demographic, medical, and hereditary information.
Dataset Features
- age: Age of the individual (in years).
- pregnancy_no: Number of pregnancies.
- weight: Weight of the individual (in kg).
- height: Height of the individual (in cm).
- bmi: Body Mass Index (BMI) of the individual.
- heredity: Family history of diabetes (Yes/No).
- prediction: Gestational diabetes diagnosis (Yes/No).
Distribution
Usage
This dataset can be used for the following applications:
- Gestational Diabetes Risk Prediction: Build models to predict gestational diabetes status based on demographic and medical factors.
- Health Behavior Analysis: Study the impact of BMI, heredity, and other health indicators on gestational diabetes prevalence.
- Medical Research: Explore correlations between pregnancy count, weight, and diabetes risk.
- Preventative Healthcare: Identify key predictors for early detection and prevention strategies.
- Policy and Decision Making: Provide insights into maternal health trends to guide public health initiatives.
Coverage
This synthetic dataset is anonymized and adheres to data privacy standards. It represents diverse demographics and health profiles, enabling broad applications in healthcare research and analysis.
License
CC0 (Public Domain)
Who Can Use It
- Data Scientists and Machine Learning Practitioners: For classification tasks related to gestational diabetes prediction and risk analysis.
- Healthcare Researchers: To study risk factors and prevalence of gestational diabetes in a synthetic population.
- Public Health Professionals: For insights into maternal health trends and preventative care.
- Educators and Students: As a teaching resource for analyzing health-related datasets and building predictive models.
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