Risk factors for high CAD-RADS scoring in CAD patients revealed by machine learning methods: a retrospective study.

ABSTRACT:

Objective

This study aimed to investigate a variety of machine learning (ML) methods to predict the association between cardiovascular risk factors and coronary artery disease-reporting and data system (CAD-RADS) scores.

Methods

This is a retrospective cohort study. Demographical, cardiovascular risk factors and coronary CT angiography (CCTA) characteristics of the patients were obtained. Coronary artery disease (CAD) was evaluated using CAD-RADS score. The stenosis severity component of the CAD-RADS was stratified into two groups: CAD-RADS score 0-2 group and CAD-RADS score 3-5 group. CAD-RADS scores were predicted with random forest (RF), k-nearest neighbors (KNN), support vector machines (SVM), neural network (NN), decision tree classification (DTC) and linear discriminan

SUBMITTER: Dai Y 

PROVIDER: S-EPMC10404399 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature