Machine learning models of clinically relevant biomarkers for the prediction of stable obstructive coronary artery disease.

ABSTRACT:

Background

In patients with suspected obstructive coronary artery disease (CAD), evaluation using a pre-test probability model is the key element for diagnosis; however, its accuracy is controversial. This study aimed to develop machine learning (ML) models using clinically relevant biomarkers to predict the presence of stable obstructive CAD and to compare ML models with an established pre-test probability of CAD models.

Methods

Eight machine learning models for prediction of obstructive CAD were trained on a cohort of 1,312 patients [randomly split into the training (80%) and internal validation sets (20%)]. Twelve clinical and blood biomarker features assessed on admission were used to inform the models. We compared the best-performing ML model and established the pre-tes

SUBMITTER: Kim J 

PROVIDER: S-EPMC9343708 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature