  From a fresh data science perspective, this thesis discusses the prediction
of coronary artery disease based on genetic variations at the DNA base pair
level, called Single-Nucleotide Polymorphisms (SNPs), collected from the
Ontario Heart Genomics Study (OHGS).
  First, the thesis explains two commonly used supervised learning algorithms,
the k-Nearest Neighbour (k-NN) and Random Forest classifiers, and includes a
complete proof that the k-NN classifier is universally consistent in any finite
dimensional normed vector space. Second, the thesis introduces two
dimensionality reduction steps, Random Projections, a known feature extraction
technique based on the Johnson-Lindenstrauss lemma, and a new method termed
Mass Transportation Distance (MTD) Feature Selection for discrete domains.
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.
  The comparative results demonstrate that MTD Feature Selection with Random
Forest is vastly superior to Random Projections and k-NN. The Random Forest
classifier is able to obtain an accuracy of 0.6660 and an area under the ROC
curve of 0.8562 on the OHGS genetic dataset, when 3335 SNPs are selected by MTD
Feature Selection for classification. This area is considerably better than the
previous high score of 0.608 obtained by Davies et al. in 2010 on the same
dataset.
