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Synthetic Maternal Health Records Dataset
Patient Health Records & Digital Health
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£79.99
About
This synthetic healthcare dataset has been generated as an educational resource for data science, machine learning, and data analysis applications in maternal healthcare. The data reflects real-world patient records, focusing on maternal health during pregnancy, and is designed for users to practice data manipulation, develop analytical skills, and build predictive models in the context of pregnancy risk monitoring.
Dataset Features:
- Age: Age of the woman during pregnancy (in years).
- SystolicBP: Upper value of blood pressure (in mmHg), an important indicator for maternal health during pregnancy.
- DiastolicBP: Lower value of blood pressure (in mmHg), another key attribute monitored during pregnancy.
- BS (Blood Glucose Level): Blood glucose levels, represented in mmol/L, used to track gestational diabetes risk. - BodyTemp: Body temperature (in Celsius) recorded during the pregnancy. - HeartRate: Normal resting heart rate in beats per minute, a critical parameter for maternal health. - Risk Level: Predicted risk intensity level during pregnancy, categorized as “low risk,” “mid risk,” or “high risk,” based on the previous attributes.
Distribution:
Usage:
This dataset is useful for a variety of applications, including:
Maternal Health Research: To explore trends and risk factors in maternal health during pregnancy, including the impact of blood pressure, glucose levels, and other indicators.
Educational Training: To teach data cleaning, transformation, and visualization techniques specific to maternal health data.
Predictive Modeling: To develop models that predict pregnancy risk levels based on various health factors, such as blood pressure, glucose levels, and heart rate.
Correlation Heatmap of Numerical Variables:
Coverage:
This dataset is synthetic and anonymized, making it a safe tool for experimentation and learning without compromising real patient privacy.
License:
CCO (Public Domain)
Who can use it:
- Researchers and educators: For studies or teaching purposes in healthcare analytics and data science.
- Data science enthusiasts: For learning, practising, and applying healthcare data manipulation and analysis techniques.
- Healthcare professionals: For analyzing and predicting risk factors in maternal health, improving patient care models.
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