Open Access

ARTICLE

Machine Learning Based Framework for Maintaining Privacy of Healthcare Data

by Adil Hussain Seh¹, Jehad F. Al-Amri², Ahmad F. Subahi³, Alka Agrawal¹, Rajeev Kumar^4,*, Raees Ahmad Khan¹

1 Department of Information Technology, Babasaheb Bhimrao Ambedkar University, Lucknow, 226025, India
2 Department of Information Technology, College of Computers and Information Technology, Taif University, Taif P.O. Box 11099, Taif 21944, Saudi Arabia
3 Department of Computer Science, University College of Al Jamoum, Umm Al Qura University, Makkah, 21421, Saudi Arabia
4 Department of Computer Applications, Shri Ramswaroop Memorial University, Barabanki, 225003, India

* Corresponding Author: Rajeev Kumar. Email:

Intelligent Automation & Soft Computing 2021, 29(3), 697-712. https://doi.org/10.32604/iasc.2021.018048

Received 23 February 2021; Accepted 13 April 2021; Issue published 01 July 2021

Abstract

The Adoption of Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), cloud services, web-based software systems, and other wireless sensor devices in the healthcare infrastructure have led to phenomenal improvements and benefits in the healthcare sector. Digital healthcare has ensured early diagnosis of the diseases, greater accessibility, and mass outreach in terms of treatment. Despite this unprecedented success, the privacy and confidentiality of the healthcare data have become a major concern for all the stakeholders. Data breach reports reveal that the healthcare data industry is one of the key targets of cyber invaders. In fact the last few years have registered an unprecedented rise in healthcare data breaches. Hacking incidents and privilege abuse are the most common threats and have exposed sensitive and protected health data. Experts and researchers are working on various techniques, tools, and methods to address the security issues related to healthcare data. In this article, the main focus is on evaluating the impact of research studies done in the context of healthcare data breach reports to identify the contemporary privacy and confidentiality issues of sensitive healthcare data. Analysis of the research studies depicts that there is a need for proactive security mechanisms that will help the healthcare organizations to identify abnormal user behavior while accessing healthcare data. Moreover, studies also suggest that ML techniques would be highly effective in securing the privacy and confidentiality of the healthcare data. Working further on this premise, the present study also proposes a conceptual framework that will secure the privacy and confidentiality of healthcare data proactively. The proposed framework is based on ML techniques to detect deviated user access against Electronic Health Records. Further, fuzzy-based Analytical Network Process (ANP), a multi-criteria decision-making approach, is used to assess the accuracy of the supervised and unsupervised ML approaches for achieving a dynamic digital healthcare data security environment.

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Citations