  This proof of concept (PoC) assesses the ability of machine learning (ML)
classifiers to predict the presence of a stenosis in a three vessel arterial
system consisting of the abdominal aorta bifurcating into the two common
iliacs. A virtual patient database (VPD) is created using one-dimensional pulse
wave propagation model of haemodynamics. Four different machine learning (ML)
methods are used to train and test a series of classifiers -- both binary and
multiclass -- to distinguish between healthy and unhealthy virtual patients
(VPs) using different combinations of pressure and flow-rate measurements. It
is found that the ML classifiers achieve specificities larger than 80% and
sensitivities ranging from 50-75%. The most balanced classifier also achieves
an area under the receiver operative characteristic curve of 0.75,
outperforming approximately 20 methods used in clinical practice, and thus
placing the method as moderately accurate. Other important observations from
this study are that: i) few measurements can provide similar classification
accuracies compared to the case when more/all the measurements are used; ii)
some measurements are more informative than others for classification; and iii)
a modification of standard methods can result in detection of not only the
presence of stenosis, but also the stenosed vessel.
