# On the Performance of Cyber-Biomedical Features for Intrusion Detection in Healthcare 5.0

arXiv: https://arxiv.org/abs/2506.17329 · HTML: https://mlsanity.com/paper/2506.17329/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 19 Jun 2025.

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

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Explainable AI meets Healthcare: A Study on Heart Disease Dataset](https://mlsanity.com/paper/2011.03195/) (2020)
- [Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research Directions](https://mlsanity.com/paper/2408.03335/) (2024)
- [LEMDA: A Novel Feature Engineering Method for Intrusion Detection in IoT Systems](https://mlsanity.com/paper/2404.16870/) (2024)

## Abstract (arXiv)

Healthcare 5.0 integrates Artificial Intelligence (AI), the Internet of Things (IoT), real-time monitoring, and human-centered design toward personalized medicine and predictive diagnostics. However, the increasing reliance on interconnected medical technologies exposes them to cyber threats. Meanwhile, current AI-driven cybersecurity models often neglect biomedical data, limiting their effectiveness and interpretability. This study addresses this gap by applying eXplainable AI (XAI) to a Healthcare 5.0 dataset that integrates network traffic and biomedical sensor data. Classification outputs indicate that XGBoost achieved 99% F1-score for benign and data alteration, and 81% for spoofing. Explainability findings reveal that network data play a dominant role in intrusion detection whereas biomedical features contributed to spoofing detection, with temperature reaching a Shapley values magnitude of 0.37.