# Explainable machine learning for neoplasms diagnosis via electrocardiograms: an externally validated study

arXiv: https://arxiv.org/abs/2412.07737 · HTML: https://mlsanity.com/paper/2412.07737/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 42 references that are in the map.
- First posted on arXiv 10 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [Enhancing clinical decision support with physiological waveforms -- a multimodal benchmark in emergency care](https://mlsanity.com/paper/2407.17856/) (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)
- [Electrocardiogram-based diagnosis of liver diseases: an externally validated and explainable machine learning approach](https://mlsanity.com/paper/2412.03717/) (2024)

## Built on by (most cited first, 4 of 4)

- [Benchmarking ECG FMs: A Reality Check Across Clinical Tasks](https://mlsanity.com/paper/2509.25095/) (2025)
- [Explainable and externally validated machine learning for neurocognitive diagnosis via electrocardiograms](https://mlsanity.com/paper/2502.04918/) (2025)
- [Predicting Chest Radiograph Findings from Electrocardiograms Using Interpretable Machine Learning](https://mlsanity.com/paper/2509.17674/) (2025)
- [Explainable Diabetic Retinopathy Classification Using Vision Foundation Models](https://mlsanity.com/paper/2608.28207/) (2026)

## Abstract (arXiv)

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, a widely available and non-invasive tool for diagnosing neoplasms through cardiovascular changes linked to neoplastic presence. Methods: A diagnostic pipeline combining tree-based machine learning models with Shapley value analysis for explainability was developed. The model was trained and internally validated on a large dataset and externally validated on an independent cohort to ensure robustness and generalizability. Key ECG features contributing to predictions were identified and analyzed. Results: The model achieved high diagnostic accuracy in both internal testing and external validation cohorts. Shapley value analysis highlighted significant ECG features, including novel predictors. The approach is cost-effective, scalable, and suitable for resource-limited settings, offering insights into cardiovascular changes associated with neoplasms and their therapies. Conclusions: This study demonstrates the feasibility of using ECG signals and machine learning for non-invasive neoplasm diagnosis. By providing interpretable insights into cardio-neoplasm interactions, this method addresses gaps in diagnostics and supports integration into broader diagnostic and therapeutic frameworks.