Detecting Deviations: Anomaly Detection in Healthcare IoT Data Streams Using Advanced Machine Learning Techniques

In the context of growing IoT integration in healthcare, preserving data integrity becomes paramount. This study meticulously explores the adaptability and effectiveness of diverse advanced machine learning algorithms in detecting anomalies within healthcare IoT environments. The research conducts a comprehensive analysis, encompassing performance metrics and considerations of practical applicability. Research paper provides a thorough review of anomaly detection techniques in Healthcare Internet of Things (IoT) data using machine learning. As IoT becomes more prevalent in healthcare, ensuring data integrity is crucial. The study critically assesses and compares diverse machine learning-based anomaly detection methods, evaluating their performance, adaptability, and practical implications in healthcare settings. Through an exhaustive literature survey, this research seeks to offer a consolidated understanding of the current landscape, pinpoint challenges, and propose potential directions for future research to optimize anomaly detection, ensuring heightened security and reliability in healthcare IoT environments.