This experiment uses the Heart Disease dataset from the UCI Machine Learning repository to train a model for heart disease prediction.
In this experiment, we used the training [Heart Disease data set](http://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/heart-disease.names) from the UCI Machine Learning Repository. The data was collected from several locations (Cleveland Clinic Foundation, Hungarian Institute of Cardiology, V.A. Medical Center, University Hospital Zurich). We built the heart disease classification model using data from the Cleveland Clinic (cleveland.data). The training data set used in this experiment consist of the following features: - age - sex - chestpaintype - resting_blood_pressure - serum_cholestrol - fasting_blood_sugar - resting_ecg - max_heart_rate - exercise_induced_angina - st_depression_induced_by_exercise - slope_of_peak_exercise - number_of_major_vessel - thal (results from a thallium heart scan) We focus on detecting the presence of heart disease (value 1,2,3 and 4) and absence (value 0). In the experiment, we evaluted the use of both the Two-class Support Vector Machine, and Decision Jungle classification algorithms. Created by a Microsoft Employee