Prediction of carotid artery plaque area based on parallel multi-gate attention capture model.

Abstract

Cardiovascular disease (CVD) is a group of conditions involving the heart or blood vessels and is a leading cause of death and disability worldwide. Carotid artery plaque, as a key risk factor, is crucial for the early prevention and management of CVD. The purpose of this study is to combine clinical application and deep learning techniques to design a predictive model for the carotid artery plaque area. This model aims to identify individuals at high risk and reduce the incidence of cardiovascular disease through the implementation of relevant preventive measures. This study proposes an innovative multi-gate attention capture (MGAC) model that utilizes data such as risk factors, laboratory tests, and physical examinations to predict the area of carotid artery plaque. Experimental findings reveal the superior performance of the MGAC model, surpassing other commonly used deep learning models with the following metrics: mean absolute error of 4.17, root mean square error of 10.89, mean logarithmic squared error of 0.21, and coefficient of determination of 0.98.