- Title
- Mining outlying aspects on healthcare data
- Creator
- Samariya, Durgesh; Ma, Jiangang
- Date
- 2021
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/188643
- Identifier
- vital:17322
- Identifier
-
https://doi.org/10.1007/978-3-030-90885-0_15
- Identifier
- ISBN:0302-9743 (ISSN); 9783030908843 (ISBN)
- Abstract
- Machine learning and artificial intelligence have a wide range of applications in medical domain, such as detecting anomalous reading, anomalous patient health condition, etc. Many algorithms have been developed to solve this problem. However, they fail to answer why those entries are considered as an outlier. This research gap leads to outlying aspect mining problem. The problem of outlying aspect mining aims to discover the set of features (a.k.a subspace) in which the given data point is dramatically different than others. In this paper, we present an interesting application of outlying aspect mining in the medical domain. This paper aims to effectively and efficiently identify outlying aspects using different outlying aspect mining algorithms and evaluate their performance on different real-world healthcare datasets. The experimental results show that the latest isolation-based outlying aspect mining measure, SiNNE, have outstanding performance on this task and have promising results. © 2021, Springer Nature Switzerland AG.
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- 10th International Conference on Health Information Science, HIS 2021, Melbourne, 25-28 October 2021, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13079 LNCS, p. 160-170
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2021, Springer Nature Switzerland AG
- Subject
- Healthcare; Outlier detection; Outlying aspect mining
- Reviewed
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