Cardiovascular Risk Data Analysis

Overview

This project explores cardiovascular health data to investigate the relationship between various health, lifestyle, and demographic factors and a history of heart attack among survey respondents.

Using Python, the project performs exploratory data analysis (EDA), statistical comparisons, and data visualization to identify patterns and associations within the dataset.

Objectives

• Explore and understand the cardiovascular health dataset.
• Examine the distribution of heart attack history among respondents.
• Investigate associations between heart attack history and selected cardiovascular risk factors.
• Visualize patterns using charts and statistical summaries.
• Identify variables that may be relevant for future heart attack prediction modeling.

Dataset

File: heart_2022_no_nans.csv

The dataset contains health-related survey responses, including demographic characteristics, medical history, lifestyle habits, and health indicators.

The primary outcome of interest is HadHeartAttack, which records whether a respondent reports having had a heart attack.

Credit: Kaggle Database: Indicators of Heart Disease (2022 UPDATE)

Tools and Libraries

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn

Analysis Performed

• Dataset exploration and descriptive statistics
• Missing-value and duplicate checks
• Distribution analysis of cardiovascular risk factors
• Heart attack history distribution visualization
• Crosstabulation of heart attack history against selected variables, including smoking, diabetes, angina, stroke, kidney disease, age, BMI, physical activity, and other health indicators
• Group-based heart attack rate comparisons
• Correlation analysis and heatmap visualization

Project Structure

cardiovascular-risk-data-analysis/
├── cardio_data_analysis.py
├── heart_2022_no_nans.csv
└── README.md

How to Run

1. Clone or download this repository.
2. Ensure Python is installed.
3. Install the required libraries:

pip install pandas numpy matplotlib seaborn

4. Run the analysis script:

python cardio_data_analysis.py

Ensure that heart_2022_no_nans.csv is in the same directory as the Python script.

Important Note

This project is an exploratory analysis of survey data. Observed associations do not establish causation, and the analysis is not intended for clinical diagnosis or individual medical decision-making.

Future Work

A separate project will use the same dataset to develop and evaluate a neural network for predicting reported heart attack history.

Author

Daniel Glorious Oziomachukwu