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PCOS Patient Diagnostic Data
Patient Health Records & Digital Health
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About
This dataset provides important information related to patients with Polycystic Ovary Syndrome (PCOS), a prevalent hormonal disorder affecting women of childbearing age. It comprises 1000 patient entries, each detailing five crucial features typically associated with the diagnosis and risk factors of PCOS. The data offers valuable insights into patients' health status and is ideal for exploratory data analysis, feature engineering, and the development of machine learning models aimed at predicting PCOS diagnoses.
Columns
- Age (years): Represents the patient's age, with values ranging from 18 to 45 years.
- BMI (kg/m²): Denotes the Body Mass Index, a metric of body fat derived from height and weight, with values spanning 18 to 35.
- Menstrual_Irregularity (binary): A binary indicator where '0' signifies regular menstrual cycles and '1' indicates irregular cycles.
- Testosterone_Level (ng/dL): The concentration of testosterone in the patient's blood, a key hormonal marker for PCOS, ranging from 20 to 100 ng/dL.
- Antral_Follicle_Count: The count of antral follicles observed during an ultrasound scan, ranging from 5 to 30. This feature assists in assessing ovarian reserve and the potential presence of PCOS.
- PCOS_Diagnosis (binary): The target variable, a binary indicator where '0' means no PCOS diagnosis and '1' means a PCOS diagnosis, determined by a combination of risk factors such as BMI, testosterone levels, menstrual regularity, and antral follicle count.
Distribution
The dataset is typically provided in a CSV format. It contains 1000 individual records or entries, each representing a distinct patient, and consists of 6 columns.
Usage
This dataset is well-suited for a variety of applications, including:
- Conducting exploratory data analysis to uncover patterns and relationships within PCOS patient data.
- Performing feature engineering to create new variables for improved model performance.
- Developing and evaluating machine learning models to predict PCOS diagnoses based on patient features.
- Researching the risk factors and indicators associated with Polycystic Ovary Syndrome.
Coverage
The dataset focuses on the demographic of women of reproductive age, specifically those aged between 18 and 45 years. No specific geographic or time range is indicated within the provided information.
License
CC0: Public Domain
Who Can Use It
This dataset is particularly useful for:
- Data Scientists: For building predictive models and conducting in-depth analyses.
- Medical Researchers: To study PCOS, its prevalence, and associated risk factors.
- Healthcare Professionals: To gain insights into patient indicators and diagnostic criteria.
- Students: For educational projects in statistics, data science, and machine learning, particularly in the healthcare domain.
- Machine Learning Engineers: For developing and testing diagnostic algorithms.
Dataset Name Suggestions
- PCOS Patient Diagnostic Data
- Polycystic Ovary Syndrome Indicator Dataset
- Women's Hormonal Health Data
- PCOS Risk Factor Analysis Dataset
- Reproductive Health Disorder Dataset
Attributes
Original Data Source: PCOS Patient Diagnostic Data
QUALITY