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

arXiv: https://arxiv.org/abs/2412.03717 · HTML: https://mlsanity.com/paper/2412.03717/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 4 Dec 2024.

## Builds on (most cited first, 8 of 8)

- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [When Do Neural Nets Outperform Boosted Trees on Tabular Data?](https://mlsanity.com/paper/2305.02997/) (2023)
- [Enhancing clinical decision support with physiological waveforms -- a multimodal benchmark in emergency care](https://mlsanity.com/paper/2407.17856/) (2024)
- [The harms of class imbalance corrections for machine learning based prediction models: a simulation study](https://mlsanity.com/paper/2404.19494/) (2024)
- [Using explainable AI to investigate electrocardiogram changes during healthy aging -- from expert features to raw signals](https://mlsanity.com/paper/2310.07463/) (2023)
- [Estimation of Cardiac and Non-cardiac Diagnosis from Electrocardiogram Features](https://mlsanity.com/paper/2408.17329/) (2024)
- [Abnormality Prediction and Forecasting of Laboratory Values from Electrocardiogram Signals Using Multimodal Deep Learning](https://mlsanity.com/paper/2411.14886/) (2024)
- [Explaining Time Series Classification Predictions via Causal Attributions](https://mlsanity.com/paper/2405.15871/) (2024)

## Built on by (most cited first, 2 of 2)

- [Explainable machine learning for neoplasms diagnosis via electrocardiograms: an externally validated study](https://mlsanity.com/paper/2412.07737/) (2024)
- [Explainable and externally validated machine learning for neurocognitive diagnosis via electrocardiograms](https://mlsanity.com/paper/2502.04918/) (2025)

## Abstract (arXiv)

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 predictive performance with good generalizability. For example, AUROCs for alcoholic liver disease (K70) were 0.8025 (95% confidence interval (CI), 0.8020-0.8035) internally and 0.7644 (95% CI, 0.7641-0.7649) externally; for hepatic failure (K72), scores were 0.7404 (95% CI, 0.7389-0.7415) and 0.7498 (95% CI, 0.7494-0.7509), respectively. The explainability analysis consistently identified age and prolonged QTc intervals (corrected QT, reflecting ventricular repolarization) as key predictors. Features linked to autonomic regulation and electrical conduction abnormalities were also prominent, supporting known cardiovascular-liver connections and suggesting QTc as a potential biomarker. Interpretation: ECG-based machine learning offers a promising, interpretable approach for liver disease detection, particularly in resource-limited settings. By revealing clinically relevant biomarkers, this method supports non-invasive diagnostics, early detection, and risk stratification prior to targeted clinical assessments.