Electrocardiogram-based diagnosis of liver diseases: an externally validated and explainable machine learning approach.

ABSTRACT:

Background

Liver diseases present a significant global health challenge and often require costly, invasive diagnostics. Electrocardiography (ECG), a widely available and non-invasive tool, can enable the detection of liver disease by capturing cardiovascular-hepatic interactions.

Methods

We trained tree-based machine learning models on ECG features to detect liver diseases using two large datasets: MIMIC-IV-ECG (467,729 patients, 2008-2019) and ECG-View II (775,535 patients, 1994-2013). The task was framed as binary classification, with performance evaluated via the area under the receiver operating characteristic curve (AUROC). To improve interpretability, we applied explainability methods to identify key predictive features.

Findings

The models showed strong predict

SUBMITTER: Lopez Alcaraz JM 

PROVIDER: S-EPMC12149549 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature