sis 18(18): e5

Research Article

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

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  • @ARTICLE{10.4108/eai.19-6-2018.154828,
        author={M. Sharma and G. Singh and R. Singh},
        title={An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={5},
        number={18},
        publisher={EAI},
        journal_a={SIS},
        year={2018},
        month={6},
        keywords={Diabetes, Cardiovascular, IoT, Data Mining, Semantic Analysis, Granular Computing, chatbot},
        doi={10.4108/eai.19-6-2018.154828}
    }
    
  • M. Sharma
    G. Singh
    R. Singh
    Year: 2018
    An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders
    SIS
    EAI
    DOI: 10.4108/eai.19-6-2018.154828
M. Sharma1,*, G. Singh2, R. Singh2
  • 1: DAV University, Jalandhar, India
  • 2: Guru Nanak Dev University, Amritsar
*Contact email: manik_sharma25@yahoo.com

Abstract

The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.