Machine Learning for Electrocardiographic Features to Identify Left Atrial Enlargement in Young Adults: CHIEF Heart Study.

ABSTRACT:

Background

Left atrial enlargement (LAE) is associated with cardiovascular events. Machine learning for ECG parameters to predict LAE has been performed in middle- and old-aged individuals but has not been performed in young adults.

Methods

In a sample of 2,206 male adults aged 17-43 years, three machine learning classifiers, multilayer perceptron (MLP), logistic regression (LR), and support vector machine (SVM) for 26 ECG features with or without 6 biological features (age, body height, body weight, waist circumference, and systolic and diastolic blood pressure) were compared with the P wave duration of lead II, the traditional ECG criterion for LAE. The definition of LAE is based on an echocardiographic left atrial dimension > 4 cm in the parasternal long axis window.

R

SUBMITTER: Hsu CY 

PROVIDER: S-EPMC8921457 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature