Heart Disease Dataset

Key Features of the Dataset

This dataset offers a wealth of information for exploring the factors contributing to heart disease. Below are the attributes included:

1. Age: Age of the patient in years.
2. Sex: Gender of the patient (1 = male, 0 = female).
3. Chest Pain Type: Categorized into 4 distinct types of chest pain.
4. Resting Blood Pressure: Measured in mmHg, representing the resting blood pressure of the patient.
5. Serum Cholesterol: Cholesterol level measured in mg/dl.
6. Fasting Blood Sugar: A binary attribute indicating if fasting blood sugar > 120 mg/dl (1 = True, 0 = False).
7. Resting Electrocardiographic Results: Coded as 0, 1, or 2, representing different ECG outcomes.
8. Maximum Heart Rate Achieved: Peak heart rate reached during exercise.
9. Exercise-Induced Angina: Indicates if exercise triggered angina (1 = Yes, 0 = No).
10. Oldpeak: ST depression induced by exercise relative to rest.
11. Slope of Peak ST Segment: Describes the slope of the peak exercise ST segment.
12. Major Vessels: Number of major vessels (0–3) colored by fluoroscopy.
13. Thalassemia (Thal): 0 = normal, 1 = fixed defect, 2 = reversible defect.

To maintain data confidentiality, sensitive identifiers like names and social security numbers have been replaced with dummy values.

Advantages of Using the Dataset

1. Versatility for Machine Learning:
   Ideal for classification and regression tasks, the dataset can train machine learning models to predict the likelihood of heart disease.
2. Wide Research Applications:
   Widely used in healthcare analytics, it helps researchers understand the critical factors influencing cardiovascular health.
3. Real-World Medical Data:
   Extracted from real patients, it offers insights into practical applications for diagnostics and preventive care.
4. Interdisciplinary Use:
   Suitable for medical, biological, and computational studies, enabling collaboration between fields.
5. Feature-Rich Data:
   With 14 clinically relevant attributes, the dataset supports advanced data analysis for accurate heart disease prediction.
6. Educational Resource:
   A perfect tool for students and educators to learn about data preprocessing, visualization, and predictive modeling in healthcare.

Applications of the Dataset

• Healthcare Diagnostics: Build models to predict heart disease based on patient profiles.
• Preventive Care Strategies: Analyze risk factors to design effective interventions.
• Educational Use: Ideal for teaching medical data analysis and machine learning concepts.
• Feature Engineering: Allows exploration of impactful attributes for disease prediction.
• Research Advancement: Supports cardiovascular research and innovation in medical AI.

Start Exploring the Heart Disease Dataset

This dataset serves as a cornerstone for medical and computational research in heart disease. Whether you aim to develop predictive models, analyze risk factors, or gain deeper insights into cardiovascular health, this dataset offers a rich foundation for your projects.