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Synthetic Cardiovascular Disease Prediction Dataset
Patient Health Records & Digital Health
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£179.99
About
This Synthetic Cardiovascular Disease Dataset is created for educational and research purposes in cardiology, public health, and data science. It provides demographic, medical, and diagnostic details related to cardiovascular diseases, enabling analysis of risk factors, disease progression, and treatment outcomes. The dataset can be utilized for building predictive models and exploring disease management strategies.
Dataset Features
- Age: Age of the individual in years.
- Gender: Biological sex of the individual (Male/Female).
- Smoking: Current smoking status (Yes/No).
- Hx Smoking: History of smoking (Yes/No).
- Hx Hypertension: History of hypertension (Yes/No).
- Cholesterol Level: Cholesterol level in mg/dL (e.g., Normal, Borderline, High).
- Blood Pressure: Blood pressure reading in mmHg (e.g., Normal, Elevated, Hypertension Stage 1).
- Diabetes: Presence of diabetes (Yes/No).
- BMI: Body Mass Index, a measure of body fat based on height and weight.
- Physical Activity: Physical activity level (e.g., Sedentary, Moderate, Active).
- Risk Score: Cardiovascular risk score (Low, Moderate, High).
- Angina: Presence of chest pain or discomfort (Yes/No).
- Heart Attack: Indicates if the patient has suffered a myocardial infarction (Yes/No).
- Heart Failure: Indicates the presence of heart failure (Yes/No).
- Treatment Type: Type of treatment administered (e.g., Medication, Surgery, Lifestyle Changes).
- Response: Patient's response to treatment (e.g., Excellent, Partial, Poor).
- Recurred: Indicates whether cardiovascular issues have recurred (Yes/No).
Distribution
Usage
This dataset is suited for the following applications:
- Risk Prediction: Develop predictive models to identify patients at risk of cardiovascular complications or recurrence.
- Treatment Outcome Analysis: Evaluate the effectiveness of treatments based on response and recurrence data.
- Disease Progression Modeling: Study the progression of cardiovascular diseases using features like cholesterol, hypertension, and risk scores.
- Public Health Research: Analyze demographic and clinical patterns to inform cardiovascular disease management strategies.
- Predictive Modeling: Build machine learning models to predict disease outcomes based on demographic and clinical features.
Coverage
This synthetic dataset is anonymized and adheres to data privacy standards. It is designed for research and learning purposes, with diverse cases representing varying levels of cardiovascular risk, treatment responses, and clinical conditions.
License
CC0 (Public Domain)
Who Can Use It
- Data Science Practitioners: For practicing data preprocessing, classification, and regression tasks related to cardiovascular diseases.
- Healthcare Professionals and Researchers: To explore relationships between clinical metrics and cardiovascular disease outcomes.
- Public Health Analysts: To understand trends and design strategies for managing cardiovascular diseases.
- Policy Makers and Regulators: For data-driven decision-making in cardiovascular disease prevention and treatment policies.
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