Quantile Loss Function Empowered Machine Learning Models for Predicting Carotid Arterial Blood Flow Characteristics

Abstract

This research presents a novel approach using machine learning models with the quantile loss function to predict blood flow characteristics, specifically the wall shear stress, in the common carotid artery and its bifurcated segments, the internal and external carotid arteries. The dataset for training these models was generated through a numerical model developed for the idealized artery. This model represented blood as an incompressible Newtonian fluid and the artery as an elastic pipe with varying material properties, simulating different flow conditions. The findings of this study revealed that the quantile linear regression model is the most reliable in predicting the target variable, i.e., wall shear stress in the common carotid artery. On the other hand, the quantile gradient boosting algorithm demonstrated exceptional performance in predicting wall shear stress in the bifurcated segments. Through this study, the blood velocity and the wall shear stress in the common carotid artery are identified as the most important features affecting the wall shear stress in the internal carotid artery, while the blood velocity and the blood pressure affected the same in the external carotid artery the most. Furthermore, for a given record of the feature dataset, the study revealed the efficacy of the quantile linear-regression model in capturing a possible prevalence of atherosclerotic conditions in the internal carotid artery. But then, it was not very successful in identifying the same in the external carotid artery. However, due to the use of idealized conditions in the study, these findings need comprehensive clinical verification.