# Explainable and externally validated machine learning for neurocognitive diagnosis via electrocardiograms

arXiv: https://arxiv.org/abs/2502.04918 · HTML: https://mlsanity.com/paper/2502.04918/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 50 references that are in the map.
- First posted on arXiv 7 Feb 2025.

## Builds on (most cited first, 7 of 7)

- [Enhancing clinical decision support with physiological waveforms -- a multimodal benchmark in emergency care](https://mlsanity.com/paper/2407.17856/) (2024)
- [Performance evaluation of predictive AI models to support medical decisions: Overview and guidance](https://mlsanity.com/paper/2412.10288/) (2024)
- [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)
- [Estimation of Cardiac and Non-cardiac Diagnosis from Electrocardiogram Features](https://mlsanity.com/paper/2408.17329/) (2024)
- [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)

## Abstract (arXiv)

Background: Electrocardiogram (ECG) analysis has emerged as a promising tool for detecting physiological changes linked to non-cardiac disorders. Given the close connection between cardiovascular and neurocognitive health, ECG abnormalities may be present in individuals with co-occurring neurocognitive conditions. This highlights the potential of ECG as a biomarker to improve detection, therapy monitoring, and risk stratification in patients with neurocognitive disorders, an area that remains underexplored. Methods: We aim to demonstrate the feasibility to predict neurocognitive disorders from ECG features across diverse patient populations. We utilized ECG features and demographic data to predict neurocognitive disorders defined by ICD-10 codes, focusing on dementia, delirium, and Parkinson's disease. Internal and external validations were performed using the MIMIC-IV and ECG-View datasets. Predictive performance was assessed using AUROC scores, and Shapley values were used to interpret feature contributions. Results: Significant predictive performance was observed for disorders within the neurcognitive disorders. Significantly, the disorders with the highest predictive performance is F03: Dementia, with an internal AUROC of 0.848 (95% CI: 0.848-0.848) and an external AUROC of 0.865 (0.864-0.965), followed by G30: Alzheimer's, with an internal AUROC of 0.809 (95% CI: 0.808-0.810) and an external AUROC of 0.863 (95% CI: 0.863-0.864). Feature importance analysis revealed both known and novel ECG correlates. ECGs hold promise as non-invasive, explainable biomarkers for selected neurocognitive disorders. This study demonstrates robust performance across cohorts and lays the groundwork for future clinical applications, including early detection and personalized monitoring.