Cardiovascular Disease Dataset Description

Overview

This dataset consolidates information from two primary sources:

1. UCI Machine Learning Repository - Heart Disease Dataset
2. Kaggle - Heart Disease Dataset by YasserH

The primary aim is to predict the presence or absence of cardiovascular disease based on various patient metrics.

Variables

• ID: Unique identifier for each patient.
• age: Age of the patient in days.
• age_years: Age of the patient in years (derived from age).
• gender: Gender of the patient. Categorical variable (1: Female, 2: Male).
• height: Height of the patient in centimeters.
• weight: Weight of the patient in kilograms.
• ap_hi: Systolic blood pressure.
• ap_lo: Diastolic blood pressure.
• cholesterol: Cholesterol levels. Categorical variable (1: Normal, 2: Above Normal, 3: Well Above Normal).
• gluc: Glucose levels. Categorical variable (1: Normal, 2: Above Normal, 3: Well Above Normal).
• smoke: Smoking status. Binary variable (0: Non-smoker, 1: Smoker).
• alco: Alcohol intake. Binary variable (0: Does not consume alcohol, 1: Consumes alcohol).
• active: Physical activity. Binary variable (0: Not physically active, 1: Physically active).
• cardio: Presence or absence of cardiovascular disease. Target variable. Binary (0: Absence, 1: Presence).
• bmi: Body Mass Index, derived from weight and height. Calculated as ( \text{BMI} = \frac{\text{weight (kg)}}{\text{height (m)}^2} ).
• bp_category: Blood pressure category based on ap_hi and ap_lo. Categories include "Normal", "Elevated", "Hypertension Stage 1", "Hypertension Stage 2", and "Hypertensive Crisis".
• bp_category_encoded: Encoded form of bp_category for machine learning purposes.

Data Source and Acknowledgment

• Data was sourced from the UCI Machine Learning Repository and Kaggle.
• All patient data has been anonymized to ensure privacy.