Anomaly-based threat detection in smart health using machine learning
The experimental results reveal that Isolation Forest SVM emerges as the top performer showcasing the effectiveness of these models in anomaly detection tasks. The proposed methodology utilizing isolation forest and SVM produced better results by detecting anomalies with less false positives in this specific EHR of a hospital in North England. Furthermore the proposal is also able to identify new contextual anomalies that were not identified in the baseline methodology.
Authors
- Meher Tabassum
- Bader Khalid Alshemaimri (ORCID: https://orcid.org/0009-0004-8865-5438)
- Saba Mahmood (ORCID: https://orcid.org/0000-0003-2758-1901)
- Amal Bukhari (ORCID: https://orcid.org/0000-0003-4888-6253)
- Ali Daud (ORCID: https://orcid.org/0000-0002-8284-6354)
- Fatima Khalique
Institutions
- King Saud University (SA)
- University of Jeddah (SA)
- Bahria University (PK)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2024-11-19
- DOI
- https://doi.org/10.1186/s12911-024-02760-4
- Citations
- 42
- Primary Topic
- Anomaly Detection Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 14.59