Affiliations: Research Groups on Intelligent Machines (REGIM), National School of Engineers (ENIS), University of Sfax, Sfax, Tunisia
Correspondence:
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Corresponding author. Hanen Jemal, REsearch Groups on Intelligent Machines (REGIM), National School of Engineers (ENIS), University of Sfax, Sfax, Tunisia. E-mail: [email protected].
Abstract: The present study is intended to provide a practical Decision Support System framework, based on Multi Agent System (MAS) and Intuitionistic Fuzzy Logic (IFL), useful for implementation in the healthcare area. The major objective consists in enabling the implementing of the conceived incremental project design, dubbed “smart healthcare”, in public or private healthcare centers (hospitals, polyclinics, etc.) through mobile cloud computing technology. In this regard, the present work is conceived to involve a thorough investigation and discussion of the IFL efficiency scope, as integrated into MAS architecture, for the purpose of detecting the patient’s health status in Intensive Care Units (ICUs). To this end, an implementation of the Intuitionistic Fuzzy Logic Decision Support System (IFLDSS), based on the Modified Early Warning Score (MEWS) standard, is carried out in the Tunisian Sfax city based ESSALEMA polyclinic. The proposed solution’s achieved results appears to reveal that the IFLDSS proves to display a commanding capacity in detecting uncertainty related to the ICU patients’ deteriorating cases. On comparing the IFL provided performance to that exhibited by the IFLDSS application, the findings appears to reveal well that the IFL MEWS appears to achieve remarkably higher accuracy scores than those provided by the MEWS standard.
Keywords: Medical diagnostic decision, multi agent system, intuitionistic fuzzy logic
decision support system, smart healthcare, intensive care unit, mobile cloud computing, accuracy
