Predicting early-stage coronary artery disease using machine learning and routine clinical biomarkers improved by augmented virtual data.

ABSTRACT:

Aims

Coronary artery disease (CAD) is a highly prevalent disease with modifiable risk factors. In patients with suspected obstructive CAD, evaluating the pre-test probability model is crucial for diagnosis, although its accuracy remains controversial. Machine learning (ML) predictive models can help clinicians detect CAD early and improve outcomes. This study aimed to identify early-stage CAD using ML in conjunction with a panel of clinical and laboratory tests.

Methods and results

The study sample included 3316 patients enrolled in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study. A comprehensive array of attributes was considered, and an ML pipeline was developed. Subsequently, we utilized five approaches to generating high-quality virtual patient data to impr

SUBMITTER: Koloi A 

PROVIDER: S-EPMC11417487 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature