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Synthetic Fertility Monitoring Records Dataset
Patient Health Records & Digital Health
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£79.99
About
This synthetic Fertility Diagnosis Dataset is designed to support educational and research applications in data science, machine learning, and health analytics. The dataset provides detailed information about factors that may influence fertility, such as age, medical history, lifestyle habits, and activity levels. It is useful for building predictive models, conducting health assessments, and exploring patterns in fertility-related health outcomes.
Dataset Features:
- Season: The season when the data was recorded (Winter, Spring, Summer, Autumn).
- Age: Age of the individual (in years).
- Childish Diseases: History of childhood diseases (Yes, No).
- Accident or Serious Trauma: History of accidents or serious trauma (Yes, No).
- Surgical Intervention: History of surgical interventions (Yes, No).
- High Fevers in the Last Year: Occurrence of high fevers within the last year (No, More than 3 months ago, Less than 3 months ago).
- Frequency of Alcohol Consumption: Frequency of alcohol intake (Hardly ever or never, Once a week, Several times a week, Every day).
- Smoking Habit: Smoking status (Never, Occasional, Daily).
- Number of Hours Spent Sitting Per Day: Average hours spent sitting daily (numeric).
- Diagnosis: Fertility diagnosis label (Normal, Altered).
Coverage:
Usage:
This dataset is valuable for various applications, including:
- Fertility Analysis: To explore the relationship between lifestyle, medical history, and fertility outcomes, and to build models that classify fertility status.
- Health Risk Assessment: To assess the health risks associated with fertility issues and identify contributing factors such as medical history and lifestyle habits.
- Predictive Modeling: To develop models that predict fertility status (Normal or Altered) based on individual data.
- Healthcare Research: To investigate patterns and trends in fertility-related factors across different demographics.
- Public Health Policy: To analyze trends and guide interventions aimed at improving reproductive health and addressing factors affecting fertility.
Coverage:
This dataset is synthetic and anonymized, making it suitable for experimentation and learning without concerns about real patient data.
License:
CC0 (Public Domain)
Who Can Use It:
- Data Science Learners: Ideal for practicing data cleaning, manipulation, and building classification models.
- Healthcare Professionals and Researchers: Useful for studying fertility-related trends and contributing to reproductive health research.
- Medical Analysts: For developing models to predict fertility outcomes and identify treatment strategies.
- Public Health Officials: For analyzing fertility trends and making data-driven decisions to improve reproductive health outcomes.
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