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Unsupervised Anomaly Detection of Healthcare Providers using Generative Adversarial Network
Dissertation (MSc (Computer Science))--University of Pretoria, 2021.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2021
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| _version_ | 1867613466266697728 |
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| access_status_str | Open Access |
| author2 | Marivate, Vukosi |
| author_browse | Marivate, Vukosi |
| author_facet | Marivate, Vukosi |
| collection | Thesis |
| dc_rights_str_mv | © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc (Computer Science))--University of Pretoria, 2021. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/78373 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:35.732Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/78373 Unsupervised Anomaly Detection of Healthcare Providers using Generative Adversarial Network Marivate, Vukosi marionaidoo@gmail.com Naidoo, Krishnan UCTD Deep Learning Generative Adversarial Network (GAN) Anomaly Detection Healthcare providers Machine Learning Engineering, built environment and information technology theses SDG-03 Engineering, built environment and information technology theses SDG-09 Engineering, built environment and information technology theses SDG-16 Dissertation (MSc (Computer Science))--University of Pretoria, 2021. Healthcare fraud is considered a challenge for many societies. Healthcare funding that could be spent on medicine, care for the elderly or emergency room visits is instead lost to fraudulent activities by medical practitioners or patients. With rising healthcare costs, healthcare fraud is a major factor in increasing healthcare costs. This study evaluates previous anomaly detection machine learning models and proposes an unsupervised framework to identify anomalies using a Generative Adversarial Network (GAN) model. The GAN anomaly detection model was applied to two different healthcare provider data sets. The anomalous healthcare providers were further analysed through the application of classification models with the logistic regression and extreme gradient boosting models showing acceptable performances. Results from the SHapley Additive exPlanation also shows the predictors used to explain the anomalous healthcare providers. bs2026 Computer Science MSc (Computer Science) Unrestricted SDG-03: Good health and well-being SDG-09: Industry, innovation and infrastructure SDG-16: Peace, justice and strong institutions 2021-02-10T06:48:15Z 2021-02-10T06:48:15Z 2021-05 2021 Thesis * A2021 http://hdl.handle.net/2263/78373 en © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Deep Learning Generative Adversarial Network (GAN) Anomaly Detection Healthcare providers Machine Learning Engineering, built environment and information technology theses SDG-03 Engineering, built environment and information technology theses SDG-09 Engineering, built environment and information technology theses SDG-16 Unsupervised Anomaly Detection of Healthcare Providers using Generative Adversarial Network |
| title | Unsupervised Anomaly Detection of Healthcare Providers using Generative Adversarial Network |
| title_full | Unsupervised Anomaly Detection of Healthcare Providers using Generative Adversarial Network |
| title_fullStr | Unsupervised Anomaly Detection of Healthcare Providers using Generative Adversarial Network |
| title_full_unstemmed | Unsupervised Anomaly Detection of Healthcare Providers using Generative Adversarial Network |
| title_short | Unsupervised Anomaly Detection of Healthcare Providers using Generative Adversarial Network |
| title_sort | unsupervised anomaly detection of healthcare providers using generative adversarial network |
| topic | UCTD Deep Learning Generative Adversarial Network (GAN) Anomaly Detection Healthcare providers Machine Learning Engineering, built environment and information technology theses SDG-03 Engineering, built environment and information technology theses SDG-09 Engineering, built environment and information technology theses SDG-16 |
| url | http://hdl.handle.net/2263/78373 |