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Synthetic Menstrual Health Patient Check Dataset
Patient Health Records & Digital Health
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£179.99
About
This synthetic Menstrual Health Dataset is designed for educational and research purposes in the fields of data science, healthcare, and women's health. The dataset includes key features such as cycle length, ovulation day, menses duration, and BMI, enabling the study and prediction of menstrual health patterns and conditions.
Dataset Features
- Number of Peak: The number of menstrual cycle peaks observed (e.g., 1, 2, 3).
- Age: The age of the individual.
- Length of Cycle: The number of days between the start of one menstrual cycle and the start of the next.
- Estimated Day of Ovulation: The estimated day of ovulation during the menstrual cycle.
- Length of Leutal Phase: The number of days in the luteal phase of the menstrual cycle, the time between ovulation and menstruation.
- Length of Menses: The duration (in days) of menstrual bleeding.
- Unusual Bleeding: Indicates whether the individual experiences unusual bleeding (e.g., yes, no).
- Height: The individual's height (e.g., 5'2", 5'3").
- Weight: The individual's weight.
- BMI: The body mass index (BMI) of the individual.
- Mean of Length of Cycle: The average length of the menstrual cycle for the individual.
- Menses Score: A score related to the overall experience of menstruation, based on a set of factors such as duration, severity, and symptoms.
Coverage
Usage
This dataset is ideal for various applications in women's health and menstrual health studies:
- Menstrual Cycle Pattern Prediction: Build machine learning models to predict the length and characteristics of an individual's menstrual cycle based on demographic and health factors.
- Health Condition Analysis: Study the relationships between menstrual health, age, BMI, and unusual symptoms like unusual bleeding.
- Personalized Health Recommendations: Develop models for personalized menstrual health advice based on cycle patterns and overall health.
- Menstrual Disorder Detection: Investigate the occurrence of irregularities such as unusual bleeding and how they relate to other factors like BMI or cycle length.
- Health Research: Use the dataset to explore the impact of height, weight, and other factors on menstrual health across various age groups.
Coverage
This dataset provides synthetic, anonymized data to ensure privacy and ethical use. It includes diverse menstrual health conditions and demographics, making it suitable for comprehensive analysis across different populations and geographic regions. Key areas of coverage include:
- Age Groups: Covers a wide range of age groups, from adolescence to menopause.
- Health Variations: Includes individuals with varying BMI, menstrual lengths, and other health-related attributes.
- Cycle Diversity: Represents individuals with regular, irregular, and unique cycle patterns.
- Symptom Analysis: Incorporates data on unusual bleeding and menses-related experiences, enabling detailed exploration of menstrual disorders.
- Global Representation: Provides data reflecting individuals from different countries and regions, facilitating cross-cultural comparisons in menstrual health research.
License
CC0 (Public Domain)
Who Can Use It
- Data Science Practitioners: For practicing data preprocessing, classification, regression, and clustering tasks related to menstrual health.
- Healthcare Researchers: To explore menstrual health patterns and factors influencing menstrual cycles.
- Medical Professionals: To gain insights into menstrual health and how factors like BMI and age impact menstrual cycles.
- Public Health Analysts: To study trends in menstrual health across different populations and identify key factors influencing cycle patterns.
- Women's Health Advocates: To design strategies for improving menstrual health awareness and care based on data-driven insights.
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