  Pulmonary Hypertension (PH) is a severe disease characterized by an elevated
pulmonary artery pressure. The gold standard for PH diagnosis is measurement of
mean Pulmonary Artery Pressure (mPAP) during an invasive Right Heart
Catheterization. In this paper, we investigate noninvasive approach to PH
detection utilizing Magnetic Resonance Imaging, Computer Models and Machine
Learning. We show using the ablation study, that physics-informed feature
engineering based on models of blood circulation increases the performance of
Gradient Boosting Decision Trees-based algorithms for classification of PH and
regression of values of mPAP. We compare results of regression (with
thresholding of estimated mPAP) and classification and demonstrate that metrics
achieved in both experiments are comparable. The predicted mPAP values are more
informative to the physicians than the probability of PH returned by
classification models. They provide the intuitive explanation of the outcome of
the machine learning model (clinicians are accustomed to the mPAP metric,
contrary to the PH probability).
