Exploring the use of different ML classifier algorithms in identifying Heart Disease based on patient information
Dan Krasnonosenkikh

1. k Nearest Neighbors
2. Linear SVM
3. RBF SVM
4. Gaussian Process
5. Decision Tree
6. Random Forest 7. Neural Net
7. AdaBoost
8. Naive Bayes
9. QDA

Heart Disease Dataset

1. Data Preprocessing / Preparation (cleaning, normalization, transformation):
2. Feature Extraction (combining existing features to produce a more useful one):
3. Feature Selection (selecting the most useful features to train on among the existing ones): The database linked above contains 76 attributes, but all published experiments refer to using a subset of 14 of them. Specific Information available in the link under Dataset.