Electrocardiogram machine learning for detection of cardiovascular disease in African Americans: the Jackson Heart Study.

ABSTRACT:

Aims

Almost half of African American (AA) men and women have cardiovascular disease (CVD). Detection of prevalent CVD in community settings would facilitate secondary prevention of CVD. We sought to develop a tool for automated CVD detection.

Methods and results

Participants from the Jackson Heart Study (JHS) with analysable electrocardiograms (ECGs) (n=3679; age, 6212 years; 36% men) were included. Vectorcardiographic (VCG) metrics QRS, T, and spatial ventricular gradient vectors magnitude and direction, and traditional ECG metrics were measured on 12-lead ECG. Random forests, convolutional neural network (CNN), lasso, adaptive lasso, plugin lasso, elastic net, ridge, and logistic regression models were developed in 80% and validated in 20% samples. We compared model

SUBMITTER: Pollard JD 

PROVIDER: S-EPMC8139412 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature