Data Mining and Machine Learning with Orange

My dataset as I said is a template in the Orange software and does not come with a data dictionary to explain the data or table to me. I did research on it to gain a better understanding of the data. Below is what each column of the dataset represents: diameter narrowing: Presence of heart disease (0 = no, 1 = yes) age: Age of the individual gender: Gender of the individual (male/female) chest pain: Type of chest pain (typical angina, atypical angina, non-anginal pain, asymptomatic) rest SBP: Resting systolic blood pressure cholesterol: Serum cholesterol in mg/dl fasting blood sugar greater than 120: Fasting blood sugar greater than 120 mg/dl (1 = true; 0 = false) rest ECG: Resting electroencephalographic results (normal, ST-T wave abnormality, left ventricular hypertrophy) max HR: Maximum heart rate achieved exerc ind ang: Exercise induced angina (1 = yes; 0 = no) ST by exercise: ST depression induced by exercise relative to rest slope peak exc ST: The slope of the peak exercise ST segment (upsloping, flat, downsloping) major vessels coloured: Number of major vessels (0-3) coloured by fluoroscopy thal: Results of thallium stress test (normal, fixed defect, reversible defect)