Using machine learning to predict acute myocardial infarction and ischemic heart disease in primary care cardiovascular patients.

ABSTRACT:

Background

Early recognition, which preferably happens in primary care, is the most important tool to combat cardiovascular disease (CVD). This study aims to predict acute myocardial infarction (AMI) and ischemic heart disease (IHD) using Machine Learning (ML) in primary care cardiovascular patients. We compare the ML-models' performance with that of the common SMART algorithm and discuss clinical implications.

Methods and results

Patient-level medical record data (n = 13,218) collected between 2011-2021 from 90 GP-practices were used to construct two random forest models (one for AMI and one for IHD) as well as a linear model based on the SMART risk prediction algorithm as a suitable comparator. The data contained patient-level predictors, including demographics, procedures

SUBMITTER: Salet N 

PROVIDER: S-EPMC11257251 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature