Enhancing healthcare data security through machine learning: A data mining perspective
1 Department of Computer Science, Botho University, Gaborone, Botswana.
2 Department of Quality Assurance, Programme and Institutional Accreditation, ABM University College, Gaborone, Botswana.
Review
Open Access Research Journal of Multidisciplinary Studies, 2026, 11(01), 107-114.
Article DOI: 10.53022/oarjms.2026.11.1.0024
Publication history:
Received on 12 February 2026; revised on 22 March 2026; accepted on 24 March 2026
Abstract:
Globally the health industry has grown rapidly and turn out to be problematic day by day. Building a strong and secure healthcare system is challenging and it may lead to systematizing the health marketplace. At present, healthcare organizations can offer their stakeholders better and lower-priced services of implementing the Electronic Health Record (EHR) that rolled out the manual-based systems. This transaction made possible by innovations in ICTs. Data mining has made a big change in computer industries with the support of Data Mining. The value of information can be increased by supporting enormous data volumes with implementation of machine learning algorithms to efficiently use the data. In order to provide regular health services, personal information and health-related data must be recorded. These data are strongly related to user privacy and confidential information. Data impairment may result from improper disclosure, loss of data integrity, or inaccessibility. Data miners should have a fundamental awareness of healthcare information privacy, information security, and network security to minimize the risk of harm to people, their organizations, or themselves. The concepts, elements, and guidelines for controlling the security and privacy of healthcare information utilized for data mining are examined in this paper.
Keywords:
Data Mining; Electronic Health Record (HER); Healthcare Environment; Information and Communication Technology (ICT); Information Security; Machine Learning; Network Security
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Copyright © 2026 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0