Applying Supervised Learning Algorithms and a New Feature Selection Method to Predict Coronary Artery Disease

no code implementations • 3 Feb 2014 • Hubert Haoyang Duan

Then, this thesis compares the performance of Random Projections with the k-NN classifier against MTD Feature Selection and Random Forest, for predicting artery disease based on accuracy, the F-Measure, and area under the Receiver Operating Characteristic (ROC) curve.

Dimensionality Reduction feature selection +1