Citation

Awais, Muhammad and Fatima, Syeda Samar and Iqbal, Jawaid (2025) Review on Secure and Efficient IoT-based Healthcare System with the Integration of Machine Learning and Firewalls. Journal of Informatics and Web Engineering, 4 (3). pp. 140-152. ISSN 2821-370X

Text 1657-Article Text-21509-1-10-20250914.pdf - Published Version Restricted to Repository staff only Download (601kB)

Abstract

The integration of the Internet of Things (IoT) into healthcare would mean a revolutionized approach in patient monitoring, diagnosis, and treatment, making this quite some development in healthcare delivery. This review has focused on how the integration of IoT with Machine Learning (ML) and stringent security measures tackle the challenging situation of data privacy and cyber threats in healthcare. Current methodologies point toward how essential advanced sensors, cloud computing, and wireless technologies for IoT-based healthcare systems necessary to secure patient data. patient record kept in files and now forward to the cloud database system so that in any case of emergency it could access and keep safe from cyber-attacks, and no one can breach the security of data only authorized user can access. To achieve this security, concern firewalls, encryption technologies are used. These protection systems are applied to block unauthorized access, protect data communication channels, and make private patient information confidential always. IoT-based, ML-enabled systems perform way better in real-time monitoring, predictive analysis, and personalized treatment in contrast with conventional healthcare strategies. This discussion delineates the need for implementation of firewalls and encryption techniques for data security and patient privacy. This critical review underlines that while IoT truly has enormous potential to change healthcare, it will require continuous innovation and rigorous security protocols to help maximize these benefits.

Item Type:	Article
Uncontrolled Keywords:	Wearable sensors, remote patient monitoring, cloud computing, machine learning,
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Depositing User:	Nor Afiqah Mohd Adnan
Date Deposited:	11 Nov 2025 01:15
Last Modified:	11 Nov 2025 01:15
URII:	http://shdl.mmu.edu.my/id/eprint/14845

Downloads

Edit (login required)