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Heart Disease Risk Factors Dataset
Patient Health Records & Digital Health
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Free
About
This dataset is designed to facilitate the prediction of heart disease by offering a collection of individual-level information. It encompasses demographic details, such as age and gender, alongside medical histories, lifestyle factors, and symptoms commonly associated with heart conditions. The primary objective is to determine whether an individual has received a heart disease diagnosis, making this dataset highly suitable for exploratory data analysis and the development of machine learning models.
Columns
- Age: Age of the individual in years.
- Gender: Gender of the individual (Male/Female).
- Cholesterol: Cholesterol level in mg/dL.
- Blood Pressure: Systolic blood pressure in mmHg.
- Heart Rate: Heart rate in beats per minute.
- Smoking: Smoking status (Never/Former/Current).
- Alcohol Intake: Frequency of alcohol intake (None/Moderate/Heavy).
- Exercise Hours: Hours of exercise undertaken per week.
- Family History: Indicates a family history of heart disease (Yes/No).
- Diabetes: Status regarding diabetes (Yes/No).
- Obesity: Status regarding obesity (Yes/No).
- Stress Level: Stress level measured on a scale of 1 to 10.
- Blood Sugar: Fasting blood sugar level in mg/dL.
- Exercise Induced Angina: Presence of angina triggered by exercise (Yes/No).
- Chest Pain Type: The type of chest pain experienced (Typical Angina/Atypical Angina/Non-anginal Pain/Asymptomatic).
- Heart Disease: The target variable, indicating the presence (1: Yes) or absence (0: No) of heart disease.
Distribution
The dataset is formatted as a CSV file and is named
heart_disease_dataset.csv. It comprises 1,000 individual records across 16 distinct columns, with a file size of 73.82 kB. All columns within the dataset are fully populated, ensuring no missing values for analysis. Demographically, the dataset includes an equal distribution of genders, with 50% female and 50% male individuals. Ages range from 25 to 79 years, with an average age of 52.3 years.Usage
- Developing and evaluating machine learning models for heart disease prediction.
- Conducting exploratory data analysis to uncover significant risk factors and trends related to heart health.
- Supporting medical research into the underlying causes and determinants of cardiovascular health.
- Informing public health initiatives focused on risk assessment, prevention strategies, and health education.
- Utilising for educational purposes in data science, machine learning, and healthcare analytics courses.
Coverage
The dataset's scope is focused on individual-level health characteristics. Demographically, it includes an even split of Males and Females, with ages spanning from 25 to 79 years. The dataset does not specify a particular geographic region or a defined time range for data collection.
License
CC0: Public Domain
Who Can Use It
- Data Scientists and Machine Learning Engineers: Ideal for building, training, and validating predictive models for cardiac conditions.
- Medical Researchers: Can use the data to explore correlations between various health factors and heart disease outcomes.
- Public Health Analysts: Suitable for assessing population-level heart disease risks and designing intervention programmes.
- Students and Academics: An excellent resource for academic projects, dissertations, and learning applications in health informatics and data analytics.
Dataset Name Suggestions
- Heart Disease Risk Factors Dataset
- Cardiovascular Health Prediction Data
- Patient Heart Condition Indicators
- Medical Heart Disease Prediction Dataset
Attributes
Original Data Source: Heart Disease Risk Factors Dataset
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