Heart Disease Health Indicators Dataset

Dataset Overview

Title

• Heart Disease Prediction and Risk Factors

Source

• Dataset from the Behavioral Risk Factor Surveillance System (BRFSS).
• Link: Heart Disease Health Indicators Dataset

Objectives

• Determine the strongest risk factors for heart disease.
• Explore correlations among risk factors.
• Attempt to predict heart disease given the presence of risk indicators.

Data Processing

• Data Cleaning: Check for inconsistencies, handle missing values, and use outlier analysis for trimming and smoothing.
• Pre‑processing: Ensure reliability and interpretability, perform dimensionality reduction and transformation.
• Integration: Conduct correlation analysis and resolve conflicts during detection.

Tools

• Programming: Python / R
• Data handling: pandas
• Visualization: matplotlib, seaborn, graphviz
• Analysis: scikit‑learn

Research Questions

1. Can heart disease be accurately predicted from common health indicators?
2. Which attributes are most influential for coronary heart disease risk?
3. How can this information be made more accessible to the public?

Applications

• Developed a simple web app based on the best model, allowing users to input seven statistically significant high‑impact factors to receive a high or low risk assessment for heart disease.