Comparison of discrimination and calibration performance of ECG-based machine learning models for prediction of new-onset atrial fibrillation.

ABSTRACT:

Background

Machine learning (ML) methods to build prediction models starting from electrocardiogram (ECG) signals are an emerging research field. The aim of the present study is to investigate the performances of two ML approaches based on ECGs for the prediction of new-onset atrial fibrillation (AF), in terms of discrimination, calibration and sample size dependence.

Methods

We trained two models to predict new-onset AF: a convolutional neural network (CNN), that takes as input the raw ECG signals, and an eXtreme Gradient Boosting model (XGB), that uses the signal's extracted features. A penalized logistic regression model (LR) was used as a benchmark. Discrimination was evaluated with the area under the ROC curve, while calibration with the integrated calibration index. We

SUBMITTER: Baj G 

PROVIDER: S-EPMC10363301 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature