  Coronary Artery Disease (CAD) results from plaque deposit in a coronary
artery. Early diagnosis is imperative, so a non-invasive detection method is
being developed to identify acoustic signals caused by partial occlusions in
the artery. The blood flow in the artery is disturbed and imposes oscillatory
stresses on the artery wall. The deformations caused by the stresses can be
detected at the chest surface. Therefore, by using data simulating these
surface signals, which arise from randomly assigned source positions, machine
learning (ML) can be utilised to predict the source of the occlusion. Seven ML
algorithms were investigated, and the results from this study found that an
ensemble model combining k-Nearest Neighbours and Random Forest had the best
performance. The metrics used to evaluate this was the mean squared error and
Euclidean distance.
