- Title
- Anomaly detection on health data
- Creator
- Samariya, Durgesh; Ma, Jiangang
- Date
- 2022
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/190355
- Identifier
- vital:17609
- Identifier
-
https://doi.org/10.1007/978-3-031-20627-6_4
- Identifier
- ISBN:0302-9743 (ISSN); 9783031206269 (ISBN)
- Abstract
- The identification of anomalous records in medical data is an important problem with numerous applications such as detecting anomalous reading, anomalous patient health condition, health insurance fraud detection and fault detection in mechanical components. This paper compares the performances of seven state-of-the-art anomaly detection algorithms to do detect anomalies in healthcare data. Our experimental results in six datasets show that the state-of-the-art method of isolation based method iForest has a better performance overall in terms of AUC and runtime. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- 11th International Conference on Health Information Science, HIS 2022, Virtual, Online, 28- 30 October 2022, Health Information Science, 11th International Conference, HIS 2022, Virtual Event, October 28–30, 2022, Proceedings Vol. 13705 LNCS, p. 34-41
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2022, The Author(s)
- Subject
- Anomaly; Anomaly detection; Healthcare; Machine learning
- Reviewed
- Hits: 1029
- Visitors: 295
- Downloads: 0
| Thumbnail | File | Description | Size | Format |
|---|