# Estimation of Cardiac and Non-cardiac Diagnosis from Electrocardiogram Features

arXiv: https://arxiv.org/abs/2408.17329 · HTML: https://mlsanity.com/paper/2408.17329/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 5 of 5)

- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)
- [Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL](https://mlsanity.com/paper/2004.13701/) (2020)
- [Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery](https://mlsanity.com/paper/2305.17043/) (2023)
- [Using explainable AI to investigate electrocardiogram changes during healthy aging -- from expert features to raw signals](https://mlsanity.com/paper/2310.07463/) (2023)
- [CardioLab: Laboratory Values Estimation from Electrocardiogram Features - An Exploratory Study](https://mlsanity.com/paper/2407.18629/) (2024)

## Built on by (most cited first, 5 of 5)

- [Explainable machine learning for neoplasms diagnosis via electrocardiograms: an externally validated study](https://mlsanity.com/paper/2412.07737/) (2024)
- [Electrocardiogram-based diagnosis of liver diseases: an externally validated and explainable machine learning approach](https://mlsanity.com/paper/2412.03717/) (2024)
- [Explainable and externally validated machine learning for neurocognitive diagnosis via electrocardiograms](https://mlsanity.com/paper/2502.04918/) (2025)
- [Continuous Determination of Respiratory Rate in Hospitalized Patients using Machine Learning Applied to Electrocardiogram Telemetry](https://mlsanity.com/paper/2508.15947/) (2025)
- [Predicting Chest Radiograph Findings from Electrocardiograms Using Interpretable Machine Learning](https://mlsanity.com/paper/2509.17674/) (2025)

## Abstract (arXiv)

Ensuring timely and accurate diagnosis of medical conditions is paramount for effective patient care. Electrocardiogram (ECG) signals are fundamental for evaluating a patient's cardiac health and are readily available. Despite this, little attention has been given to the remarkable potential of ECG data in detecting non-cardiac conditions. In our study, we used publicly available datasets (MIMIC-IV-ECG-ICD and ECG-VIEW II) to investigate the feasibility of inferring general diagnostic conditions from ECG features. To this end, we trained a tree-based model (XGBoost) based on ECG features and basic demographic features to estimate a wide range of diagnoses, encompassing both cardiac and non-cardiac conditions. Our results demonstrate the reliability of estimating 23 cardiac as well as 21 non-cardiac conditions above 0.7 AUROC in a statistically significant manner across a wide range of physiological categories. Our findings underscore the predictive potential of ECG data in identifying well-known cardiac conditions. However, even more striking, this research represents a pioneering effort in systematically expanding the scope of ECG-based diagnosis to conditions not traditionally associated with the cardiac system.