Prevention in Healthcare: An Explainable AI Approach

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Published: May 17, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i5.6582
Keywords:
Explainable AI, intrusion prevention, machine learning, healthcare

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Shahin Makubhai
Research Scholar, Department of Computer Science and Engineering MIT School of Engineering, MIT Art Design and Technology University Loni Kalbhor, Pune, India
Ganesh R Pathak
Professor, Department of Computer Science and Engineering MIT School of Engineering, MIT Art Design and Technology University Loni Kalbhor, Pune, India
Pankaj R Chandre
Associate Professor, Department of Computer Science and Engineering MIT School of Engineering, MIT Art Design and Technology University Loni Kalbhor, Pune, India

Abstract

Intrusion prevention is a critical aspect of maintaining the security of healthcare systems, especially in the context of sensitive patient data. Explainable AI can provide a way to improve the effectiveness of intrusion prevention by using machine learning algorithms to detect and prevent security breaches in healthcare systems. This approach not only helps ensure the confidentiality, integrity, and availability of patient data but also supports regulatory compliance. By providing clear and interpretable explanations for its decisions, explainable AI can enable healthcare professionals to understand the reasoning behind the intrusion detection system's alerts and take appropriate action. This paper explores the application of explainable AI for intrusion prevention in healthcare and its potential benefits for maintaining the security of healthcare systems.

Article Details

How to Cite
Makubhai, S. ., Pathak, G. R. ., & Chandre, P. R. . (2023). Prevention in Healthcare: An Explainable AI Approach . International Journal on Recent and Innovation Trends in Computing and Communication, 11(5), 92–100. https://doi.org/10.17762/ijritcc.v11i5.6582
Issue
Vol. 11 No. 5 (2023): May (2023) Issue
Section
Articles

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