Identification of Carotid Atherosclerosis in Medium-high Risk Population of Cardiovascular Disease: Prediction Model and Validation Based on Machine Learning

Background Carotid atherosclerosis (CAS) is often considered an early warning signal for cardiovascular diseases (CVD). The diagnostic technique of carotid artery Doppler ultrasonography has not been included in public health service programs, and the Framingham Risk Score (FRS) lacks accuracy in assessing CAS risk, hindering the identification of CAS by primary healthcare personnel. Currently, there is a lack of research on machine learning methods to identify CAS in the medium-high risk population assessed by FRS. Objective To construct a CAS risk prediction model for the medium-high risk population assessed by FRS using machine learning methods, compare its discriminative efficacy, select the optimal model, and assist primary healthcare personnel in identifying CAS more conveniently and accurately. Methods Using convenience sampling method, a total of 674 local residents from two townships in Liuzhou City, Guangxi Zhuang Autonomous Region, who met the inclusion criteria from 2019 to 2021 and 2023, were sel