sc 23(4): e2

Research Article

Medical Data Analytics and Wearable Devices

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  • @ARTICLE{10.4108/eetsc.v6i4.2264,
        author={Iswarya Manoharan and Jeslin Libisha  J and Sowmiya E C  and Harishma S. and John Amose },
        title={Medical Data Analytics and Wearable Devices},
        journal={EAI Endorsed Transactions on Smart Cities},
        volume={6},
        number={4},
        publisher={EAI},
        journal_a={SC},
        year={2022},
        month={10},
        keywords={Wearable Technology, Decision Making, Rehabilitation, Artificial Technology, Data Analytics},
        doi={10.4108/eetsc.v6i4.2264}
    }
    
  • Iswarya Manoharan
    Jeslin Libisha J
    Sowmiya E C
    Harishma S.
    John Amose
    Year: 2022
    Medical Data Analytics and Wearable Devices
    SC
    EAI
    DOI: 10.4108/eetsc.v6i4.2264
Iswarya Manoharan1,*, Jeslin Libisha J2, Sowmiya E C 3, Harishma S.4, John Amose 5
  • 1: University of Naples Federico II
  • 2: Dr. N.G.P. Institute of Technology
  • 3: Karpaga Vinayaga College of Engineering and Technology, Chennai, India
  • 4: Kalaingar Karunanidhi Institute of Technology, Coimbatore, India
  • 5: KPR Institute of Engineering and Technology
*Contact email: iswaryamanoharan1997@gmail.com

Abstract

Clinical decision-making may be directly impacted by wearable application. Some people think that wearable technologies, such as patient rehabilitation outside of hospitals, could boost patient care quality while lowering costs. The big data produced by wearable technology presents researchers with both a challenge and an opportunity to expand the use of artificial intelligence (AI) techniques on these data. By establishing new healthcare service systems, it is possible to organise diverse information and communications technologies into service linkages. This includes emerging smart systems, cloud computing, social networks, and enhanced sensing and data analysis techniques. The characteristics and features of big data, the significance of big data analytics in the healthcare industry, and a discussion of the effectiveness of several machine learning algorithms employed in big data analytics served as our conclusion.