This synthetic Heart Attack Risk Prediction Dataset is designed for educational and research purposes in the fields of data science, public health, and preventive healthcare analytics. It contains critical health and lifestyle indicators such as cholesterol, heart rate, diabetes, stress levels, and physical activity, which can be used to analyze and predict the risk of heart attacks. The dataset is ideal for building predictive models, conducting risk assessments, and exploring relationships between lifestyle factors and heart health.

Dataset Features

• Age: The age of the individual in years.
• Sex: The biological sex of the individual (Male/Female).
• Cholesterol (mg/dL): The total cholesterol level in the blood.
• Heart Rate (bpm): The individual's heart rate in beats per minute.
• Diabetes: Whether the individual has diabetes (Yes/No).
• Family History: Whether there is a family history of heart conditions (Yes/No).
• Smoking: Whether the individual smokes (Yes/No).
• Obesity: Whether the individual is obese (Yes/No).
• Alcohol Consumption: Daily alcohol consumption in units.
• Exercise Hours Per Week: Weekly hours spent exercising.
• Diet: Overall diet quality categorized as Healthy, Average, or Unhealthy.
• Previous Heart Problems: Whether the individual has a history of heart conditions (Yes/No).
• Medication Use: Whether the individual takes heart-related medications (Yes/No).
• Stress Level: Self-reported stress level on a scale of 1 (low) to 10 (high).
• Sedentary Hours Per Day: Hours spent in sedentary activities daily.
• Income: Annual income in local currency.
• BMI: Body Mass Index (kg/m²), a measure of body fat based on height and weight.
• Triglycerides (mg/dL): The level of triglycerides in the blood.
• Physical Activity Days Per Week: Days the individual engages in physical activity weekly.
• Sleep Hours Per Day: Average daily sleep duration in hours.
• Country: The country of residence.
• Continent: The continent of residence.
• Hemisphere: The hemisphere of residence (Northern/Southern).
• Heart Attack Risk: Binary classification indicating heart attack risk:
• Yes: At risk of a heart attack.
• No: Not at risk of a heart attack.

Distribution

Usage

This dataset is ideal for various heart health-related applications:

• Heart Attack Risk Prediction: Develop machine learning models to classify individuals as at risk or not at risk of a heart attack.
• Risk Factor Analysis: Identify key factors contributing to heart attack risks and prioritize lifestyle interventions.
• Predictive Modeling: Build predictive models using health and lifestyle indicators to assess heart health.
• Public Health Research: Study the relationships between health metrics, lifestyle factors, and heart attack risks.
• Preventive Healthcare: Inform public health campaigns and individual preventive measures.

Coverage

This synthetic dataset is anonymized, ensuring compliance with data privacy standards. It is designed for research and learning purposes, providing diverse health conditions and demographic data for analysis and model building.

License

CC0 (Public Domain)

Who Can Use It

• Data Science Practitioners: For practicing data preprocessing, classification, and regression tasks related to heart health.
• Healthcare Professionals and Researchers: To explore relationships between health metrics and heart attack risks.
• Public Health Analysts: To understand trends and develop interventions for reducing heart attack risks.
• Policy Makers and Regulators: For data-driven decision-making in preventive healthcare policies.