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
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20072131

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 in data distribution. It may occur in the medical domain, for example the medical sensors measuring for general healthcare or rehabilitation, which may switch their roles for ICU emergency operations when required. Detecting concept drifts becomes trickier when the class distributions in data are skewed, which is often true for medical sensors e-health data. Reactive Drift Detection Method (RDDM) is an efficient method for detecting long concepts. However, RDDM has a high error rate, and it does not handle class imbalance. We propose an Enhanced Reactive Drift Detection Method (ERDDM), which systematically generates strategies to handle concept drift with class imbalance in data streams. We conducted experiments to compare ERDDM with three contemporary techniques in terms of prediction error, drift detection delay, latency, and ability to handle data imbalance. The experimentation was done in Massive Online Analysis (MOA) on 48 synthetic datasets customized to possess the capabilities of data streams. ERDDM can handle abrupt and gradual drifts and performs better than all benchmarks in almost all experiments.