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AUDIT: AnomaloUs data Detection and Isolation approach for mobile healThcare systems
Author(s) -
Ben Amor Lamia,
Lahyani Imene,
Jmaiel Mohamed
Publication year - 2020
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12390
Subject(s) - computer science , isolation (microbiology) , audit , dimension (graph theory) , data mining , context (archaeology) , health care , resource (disambiguation) , machine learning , real time computing , data science , artificial intelligence , economics , biology , economic growth , paleontology , computer network , mathematics , management , pure mathematics , microbiology and biotechnology
Mobile health care systems highly depend on collected physiological data through medical sensors to provide high‐quality care services. However, inaccurate physiological data from sensors pose a major challenge for health care providers when making decisions, whereas an erroneous decision can affect the user's life. We propose, in this paper, an anomalous data detection and isolation approach for mobile health care systems. Our approach, called AUDIT, detects inaccurate measurements in real time and distinguishes between faults or errors and health events. To do so, we propose reduced time and space complexities algorithms based on dimension reduction within the context of resource constraints. Furthermore, a decision algorithm is proposed while exploring the spatio‐temporal correlation between physiological attributes. First, we describe our approach. Then, we give its implementation details. Finally, to demonstrate the effectiveness of our approach, we show different experiments related to its detection performances and its time and space complexities.