Explainable machine learning for neoplasms diagnosis via electrocardiograms: an externally validated study
Abstract Background Neoplasms are a major cause of mortality globally, where early diagnosis is essential for improving outcomes. Current diagnostic methods are often invasive, expensive, and inaccessible in resource-limited settings. This study explores the potential of electrocardiogram (ECG) data...
Saved in:
| Main Authors: | Juan Miguel Lopez Alcaraz, Wilhelm Haverkamp, Nils Strodthoff |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
|
| Series: | Cardio-Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40959-025-00370-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Electrocardiogram-based diagnosis of liver diseases: an externally validated and explainable machine learning approachResearch in context
by: Juan Miguel Lopez Alcaraz, et al.
Published: (2025-06-01) -
A novel XAI framework for explainable AI-ECG using generative counterfactual XAI (GCX)
by: Jong-Hwan Jang, et al.
Published: (2025-07-01) -
Validation of a handheld electrocardiogram 6 lead recorder to obtain chest lead equivalents: An Africa Heart Rhythm Association study
by: Thomas A. Slater, PhD, et al.
Published: (2025-05-01) -
Towards Transparent AI in Medicine: ECG-Based Arrhythmia Detection with Explainable Deep Learning
by: Oleksii Kovalchuk, et al.
Published: (2025-01-01) -
TFDGiniXML: A Novel Explainable Machine Learning Framework for Early Detection of Cardiac Abnormalities Based on Nonlinear Time-Frequency Distribution Gini Index Features
by: Mohamed Aashiq, et al.
Published: (2025-01-01)