  An accurate assessment of the cardiovascular system and prediction of
cardiovascular diseases (CVDs) are crucial. Measured cardiac blood flow data
provide insights about patient-specific hemodynamics, where many specialized
techniques have been developed for the visual exploration of such data sets to
better understand the influence of morphological and hemodynamic conditions on
CVDs. However, there is a lack of machine learning approaches techniques that
allow a feature-based classification of heart-healthy people and patients with
CVDs. In this work, we investigate the potential of morphological and
hemodynamic characteristics, extracted from measured blood flow data in the
aorta, for the classification of heart-healthy volunteers and patients with
bicuspid aortic valve (BAV). Furthermore, we research if there are
characteristic features to classify male and female as well as older
heart-healthy volunteers and BAV patients. We propose a data analysis pipeline
for the classification of the cardiac status, encompassing feature selection,
model training and hyperparameter tuning. In our experiments, we use several
feature selection methods and classification algorithms to train separate
models for the healthy subgroups and BAV patients. We report on classification
performance and investigate the predictive power of morphological and
hemodynamic features with regard to the classification of the defined groups.
Finally, we identify the key features for the best models.
