  This study presents an application of machine learning (ML) methods for
detecting the presence of stenoses and aneurysms in the human arterial system.
Four major forms of arterial disease -- carotid artery stenosis (CAS),
subclavian artery stenosis (SAC), peripheral arterial disease (PAD), and
abdominal aortic aneurysms (AAA) -- are considered. The ML methods are trained
and tested on a physiologically realistic virtual patient database (VPD)
containing 28,868 healthy subjects, which is adapted from the authors previous
work and augmented to include the four disease forms. Six ML methods -- Naive
Bayes, Logistic Regression, Support Vector Machine, Multi-layer Perceptron,
Random Forests, and Gradient Boosting -- are compared with respect to
classification accuracies and it is found that the tree-based methods of Random
Forest and Gradient Boosting outperform other approaches. The performance of ML
methods is quantified through the F1 score and computation of sensitivities and
specificities. When using all the six measurements, it is found that maximum F1
scores larger than 0.9 are achieved for CAS and PAD, larger than 0.85 for SAS,
and larger than 0.98 for both low- and high-severity AAAs. Corresponding
sensitivities and specificities are larger than 90% for CAS and PAD, larger
than 85% for SAS, and larger than 98% for both low- and high-severity AAAs.
When reducing the number of measurements, it is found that the performance is
degraded by less than 5% when three measurements are used, and less than 10%
when only two measurements are used for classification. For AAA, it is shown
that F1 scores larger than 0.85 and corresponding sensitivities and
specificities larger than 85% are achievable when using only a single
measurement. The results are encouraging to pursue AAA monitoring and screening
through wearable devices which can reliably measure pressure or flow-rates
