Cardiac Cohort Classification based on Morphologic and Hemodynamic Parameters extracted from 4D PC-MRI Data
This work addresses a gap in machine learning approaches for feature-based classification of cardiovascular diseases, which could aid in patient-specific diagnosis, though it appears incremental by applying existing methods to a new medical dataset.
The paper tackled the problem of classifying heart-healthy individuals and patients with bicuspid aortic valve (BAV) using morphological and hemodynamic features from 4D PC-MRI data, achieving classification performance with identified key features but without specific numerical results.
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.