Welcome to the UCI Machine Learning Repository, where you can find hundreds of datasets curated by experienced researchers for use in predictive and inferential analyses. Today, we will explore a dataset covering coronary artery disease risk factors from the Swiss Medical Center and V.A. Medical Center in Long Beach and Cleveland Clinic Foundation. This repository, collected by David W. Aha of the University of California at Irvine's School of Information and Computer Science, contains information about patients with coronary artery disease including age, sex, blood pressure, cholesterol levels, and other key variables that might be pertinent to risk assessment or prediction of outcomes for different interventions or therapies—all important factors when it comes to cardiovascular health!
Included in this dataset are attributes such as age (in years), sex (1 = male; 0 = female), chest pain type (1 = typical angina; 2 = atypical angina; 3 = non-anginal pain); 4 = asymptomatic), exercise induced angina (1 = yes; 0 = no), number of major vessels (0-3) colored by flourosopy , fasting blood sugar > 120 mg/dl (1= true; 0= false) ST depression induced by exercise relative to rest maximum heart rate achieved ,and target (0= no disease; 1-4 increasing severity). The names and social security numbers were removed from the original database but dummy values have replaced them for identification purposes.