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Architecture of a decision support system based on big data for monitoring type 2 diabetics

Abdelhakim, Boudhir Anouar; Mohamed, Ben Ahmed; Soumaya, Fellaji

International journal of intelligent enterprise. Volume 6:Number 2-4 (2019, July 15th); pp 204-216 -- Inderscience Enterprises Ltd

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  • Title:
    Architecture of a decision support system based on big data for monitoring type 2 diabetics
  • Author: Abdelhakim, Boudhir Anouar;
    Mohamed, Ben Ahmed;
    Soumaya, Fellaji
  • Found In: International journal of intelligent enterprise. Volume 6:Number 2-4 (2019, July 15th); pp 204-216
  • Journal Title: International journal of intelligent enterprise
  • Subjects: Business enterprises--Design--Periodicals; Business intelligence--Periodicals; Knowledge management--Periodicals; Organizational effectiveness--Periodicals; Organizational learning--Periodicals; big data--analytics--Hadoop--healthcare--diabetes; Dewey: 658.005
  • Rights: Licensed
  • Publication Details: Inderscience Enterprises Ltd
  • Abstract:

    Type 2 diabetes is one of chronic diseases that require continuous and real-time monitoring to prevent the occurrence of complications. First, the doctor must have information about the patient's daily life (vital signs, stress, sedentary lifestyle, physical activities, nutrition, etc.). Secondly, the prescribed treatment must be evaluated each time to test the validity of the diagnosis. To achieve this goal, a decision support system based on big data mining technology must be designed in order to have a centralised knowledge of diabetics. This system will improve the quality of monitoring and treatment from the different data collected. Thus, this paper presents an architecture of a decision support system allowing doctors to monitor the health status of their patients, based on data collected from different resources, in order to enrich the knowledge database and prescribe new treatments based on similar cases and experiences of doctors and patients belonging to this system.


  • Identifier: System Number: ETOCvdc_100086737043.0x000001; Journal ISSN: 1745-3232
  • Publication Date: 2019
  • Physical Description: Electronic
  • UIN: ETOCvdc_100086737043.0x000001

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