Mining Massive E-Health Data Streams for IoMT Enabled Healthcare Systems

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 7; p. 2131
• Main Authors: Toor, Affan Ahmed, Usman, Muhammad, Younas, Farah, M Fong, Alvis Cheuk, Khan, Sajid Ali, Fong, Simon
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 09.04.2020; MDPI
• Subjects: Accuracy; Algorithms; Batch processing; class imbalance; concept drift; Data Mining; data stream mining; Data transmission; Delivery of Health Care; Digital media; Emergency procedures; Error detection; Experimentation; Health care; Internet of Things; IoMT; machine learning; Medical equipment; Medical research; Principal components analysis; Sensors; Telemedicine; data stream mining; class imbalance; concept drift; machine learning; IoMT

With the increasing popularity of the Internet-of-Medical-Things (IoMT) and smart devices, huge volumes of data streams have been generated. This study aims to address the concept drift, which is a major challenge in the processing of voluminous data streams. Concept drift refers to overtime change...