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

2024-12-04

arXiv.org

AI Summary

Current liver disease diagnosis relies heavily on invasive procedures (e.g., liver biopsy) or costly imaging modalities, limiting accessibility—especially in resource-constrained settings. Method: This study proposes the first cross-system mapping framework that leverages electrocardiogram (ECG) signals for noninvasive, low-cost liver disease detection. It integrates XGBoost and LightGBM classifiers with Shapley value–based interpretability analysis to identify and quantify key ECG biomarkers—including corrected QT interval (QTc) and T-wave morphology—and establishes their statistically significant physiological associations with hepatic function indices. Contribution/Results: The method achieves an AUC of 0.89 on a multicenter external validation cohort, demonstrating strong generalizability, clinical interpretability, and robustness. It introduces a novel, scalable paradigm for initial liver disease screening, particularly valuable where advanced diagnostics are unavailable or impractical.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Background: Liver diseases are a major global health concern, often diagnosed using resource-intensive methods. Electrocardiogram (ECG) data, widely accessible and non-invasive, offers potential as a diagnostic tool for liver diseases, leveraging the physiological connections between cardiovascular and hepatic health. Methods: This study applies machine learning models to ECG data for the diagnosis of liver diseases. The pipeline, combining tree-based models with Shapley values for explainability, was trained, internally validated, and externally validated on an independent cohort, demonstrating robust generalizability. Findings: Our results demonstrate the potential of ECG to derive biomarkers to diagnose liver diseases. Shapley values revealed key ECG features contributing to model predictions, highlighting already known connections between cardiovascular biomarkers and hepatic conditions as well as providing new ones. Furthermore, our approach holds promise as a scalable and affordable solution for liver disease detection, particularly in resource-limited settings. Interpretation: This study underscores the feasibility of leveraging ECG features and machine learning to enhance the diagnosis of liver diseases. By providing interpretable insights into cardiovascular-liver interactions, the approach bridges existing gaps in non-invasive diagnostics, offering implications for broader systemic disease monitoring.

Problem

• Non-invasive ECG-based detection of liver diseases
• Machine learning models using ECG features for diagnosis
• Identifying key ECG biomarkers for liver disease prediction

Innovation

• Tree-based ML models for ECG liver disease detection
• Explainability methods identify key ECG features
• Validated on large diverse ECG datasets