Review Paper on Machine Learning-Based Privacy Analysis in IoT Healthcare Systems

Author(s)	Mr. Ismail Almsallat, Dr. Hari Mohan Singh
Country	India
Abstract	The accelerated advancement of Internet of Things (IoT) technologies has led to the widespread deployment of interconnected medical devices capable of continuous physiological data acquisition, real-time monitoring, and intelligent clinical decision support. While these systems enhance diagnostic accuracy and healthcare delivery efficiency, they also expose sensitive medical data to significant security and privacy risks. This review systematically examines research published between 2016 and 2024, focusing on the application of machine learning (ML) techniques to improve the functionality, reliability, and security of IoT-based healthcare systems. The study critically analyzes state-of-the-art privacy-preserving mechanisms, secure data transmission protocols, access control models, and ML-driven anomaly detection and intrusion prevention methods for protecting patient data. Furthermore, it investigates the synergistic integration of IoT and ML architectures in healthcare environments, identifies emerging technical challenges and trends, and outlines future research directions aimed at developing robust, scalable, and privacy-aware intelligent healthcare systems.
Field	Engineering
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-12-22
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.64220

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