Developing a Machine Learning Model to Detect Unauthorised Access in Electronic Health Records of HIV Patients at Grootfontein State Hospital

Authors

Elizabeth Ndamona Malulu, Namibia University of Science and Technology, Namibia

Attlee Munyaradzi Gamundani, Namibia University of Science and Technology, Namibia

Published in

IST-Africa 2026 Conference Proceedings

ISSN: 2576-8581

ISBN: 978-1-905824-76-2

DOI: https://doi.org/10.67725/IST-Africa.2026.QYPG3295

Publisher

IST-Africa Institute and IIMC International Information Management Corporation Ltd

Published in Ireland

Abstract

Electronic Health Records (EHRs) improve the quality and efficiency of healthcare but introduce risks of unauthorised access, especially for sensitive conditions such as HIV. In Namibia, limited cybersecurity capacity increases these risks in public hospitals like Grootfontein State Hospital. This study develops a machine learning approach to detect unauthorised access to HIV patient EHRs in a low-resource setting. A mixed-methods design was used, combining healthcare worker insights with a synthetic dataset that replicates realistic access patterns without exposing real patient data. Four models-Logistic Regression, Support Vector Machine, Random Forest and Extreme Gradient Boosting-were evaluated using accuracy, precision, recall and F1-score. The Random Forest model achieved the best performance, with 79% accuracy and 65% recall. The findings show that lightweight, context-aware ML models can improve EHR security monitoring and support data protection in resource-constrained healthcare environments.

Keywords

Electronic Health Records (EHRs), Unauthorised Access Detection, Machine Learning, Anomaly Detection, Cybersecurity, Insider Threat, Data Privacy, HIV Care, Low-Resource Healthcare Settings, Namibia

Cite this paper

E.N. Malulu and A.M. Gamundani (2026) "Developing a Machine Learning Model to Detect Unauthorised Access in Electronic Health Records of HIV Patients at Grootfontein State Hospital", IST-Africa 2026 Conference Proceedings, Miriam Cunningham and Paul Cunningham (Eds), IST-Africa Institute and IIMC, 2026, ISSN: 2576-8581, ISBN: 978-1-905824-76-2, https://doi.org/10.67725/IST-Africa.2026.QYPG3295

Access Paper

Click here to access this paper

IST-Africa Community Members can download papers by logging into their existing account.

If you wish to become a member of IST-Africa Community, please register your profile and you will then receive login details which you can use to access the papers.

Access to papers in the IST-Africa 2026 Conference Proceedings is for INDIVIDUAL USE only. Neither the Conference Proceedings (or individual double blind peer reviewed papers) are to be included in any public or institutional repository or processed in the context of any AI tool or large language model without prior written permission from the Publishers.

While the IST-Africa Institute and IIMC retain copyright of the Conference Proceedings, each author retains copyright to their individual paper. No paper or part thereof may be reproduced without the written permission of both the Conference Proceedings Publishers and appropriate author(s).