Model Data Description

Data set is taken from UCI Machine Learning Repository. There are 304 records with 13 features which are Age, Sex, Chest Pain, Systolic Blood Pressure, Serum cholestoral level, Fasting blood sugar, ECG test, Maximum heart rate achieved, Exercise induced angina, ST depression induced by exercise relative to rest, Slope of the peak exercise ST segment, Number of major vessels colored by flourosopy and Heart valve condition. On the basis of this Dataset Logistic Regression model was created which can give you a very good prediction (92% accuracy) about a person's Heart Disease condition. Let's try with the form presented in right pane and predict the health status.