A Machine Learning Framework for Diagnosing and Predicting the Severity of Coronary Artery Disease.

ABSTRACT:

Background

Although machine learning (ML)-based prediction of coronary artery disease (CAD) has gained increasing attention, assessment of the severity of suspected CAD in symptomatic patients remains challenging.

Methods

The training set for this study consisted of 284 retrospective participants, while the test set included 116 prospectively enrolled participants from whom we collected 53 baseline variables and coronary angiography results. The data was pre-processed with outlier processing and One-Hot coding. In the first stage, we constructed a ML model that used baseline information to predict the presence of CAD with a dichotomous model. In the second stage, baseline information was used to construct ML regression models for predicting the severity of CAD. The non-CAD p

SUBMITTER: Ainiwaer A 

PROVIDER: S-EPMC11264126 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature