Synthetic Polycystic Ovary Syndrome PCOS Patient Records Dataset

Dataset Features

• Age: Age range of the individual (e.g., Less than 20, 20–25, etc.).
• Weight_kg: Weight of the individual in kilograms (float).
• Height_ft: Height of the individual in feet (float).
• Marital_Status: Marital status of the individual (Married/Unmarried).
• PCOS: Indicates whether the individual has PCOS (Yes/No).
• Family_History_PCOS: Indicates whether the individual has a family history of PCOS (Yes/No).
• Menstrual_Irregularity: Indicates irregularities in menstrual cycles (Yes/No).
• Hormonal_Imbalance: Indicates whether the individual has a hormonal imbalance (Yes/No).
• Hyperandrogenism: Indicates symptoms of hyperandrogenism (e.g., acne, excessive hair growth) (Yes/No).
• Hirsutism: Indicates excessive hair growth (Yes/No).
• Mental_Health: Indicates mental health challenges (Yes/No).
• Conception_Difficulty: Indicates difficulties in conceiving (Yes/No).
• Insulin_Resistance: Indicates insulin resistance (Yes/No).
• Diabetes: Indicates whether the individual has diabetes (Yes/No).
• Childhood_Trauma: Indicates exposure to childhood trauma (Yes/No).
• Cardiovascular_Disease: Indicates cardiovascular disease history (Yes/No).
• Diet_Bread_Cereals: Frequency of consuming bread or cereals (integer scale).
• Diet_Milk_Products: Frequency of consuming milk products (integer scale).
• Diet_Fruits: Frequency of consuming fruits (integer scale).
• Diet_Vegetables: Frequency of consuming vegetables (integer scale).
• Diet_Starchy_Vegetables: Frequency of consuming starchy vegetables (integer scale).
• Diet_NonStarchy_Vegetables: Frequency of consuming non-starchy vegetables (integer scale).
• Diet_Fats: Frequency of consuming fats (integer scale).
• Diet_Sweets: Frequency of consuming sweets (integer scale).
• Diet_Fried_Food: Frequency of consuming fried foods (integer scale).
• Diet_Tea_Coffee: Frequency of consuming tea or coffee (integer scale).
• Diet_Multivitamin: Indicates if multivitamins are part of the diet (Yes/No).
• Vegetarian: Indicates if the individual follows a vegetarian diet (Yes/No).
• Exercise_Frequency: Frequency of exercise (e.g., Rarely, Daily, etc.).
• Exercise_Type: Type(s) of exercise (e.g., Cardio, Flexibility and balance).
• Exercise_Duration: Duration of exercise sessions (e.g., Less than 30 minutes, 30 minutes to 1 hour).
• Sleep_Hours: Average sleep duration (e.g., Less than 6 hours, 6–8 hours).
• Stress_Level: Indicates the level of stress experienced by the individual (e.g., Not at All, Somewhat).
• Smoking: Indicates whether the individual smokes (Yes/No).
• Exercise_Benefit: Indicates whether the individual has benefited from exercise (Yes/No).
• PCOS_Medication: Indicates if the individual is on medication for PCOS (Yes/No).

Distribution

Usage

• PCOS Risk Prediction: Develop machine learning models to classify individuals as at risk or not at risk of PCOS.
• Risk Factor Analysis: Identify key factors contributing to PCOS and prioritize preventive interventions.
• Predictive Modeling: Build models using demographic, lifestyle, and health indicators to assess PCOS risk.
• Public Health Research: Study relationships between dietary habits, lifestyle factors, and PCOS risk.
• Healthcare Personalization: Inform healthcare professionals about targeted treatment plans based on the dataset.
• Behavioral Insights: Analyze exercise, diet, and stress impacts on PCOS and related health outcomes.

Coverage

License

Who Can Use It

• Data Science Practitioners: For practicing data preprocessing, classification, and regression tasks related to PCOS risk.
• Healthcare Professionals and Researchers: To explore relationships between health metrics, lifestyle factors, and PCOS.
• Public Health Analysts: To understand trends and develop interventions for managing PCOS risks.
• Policy Makers and Regulators: For data-driven decision-making in women's health policies.