Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bharath University, Chennai, India

Research Article

Advanced Computer Network for Healthcare Sector – Liver Function Diagnosis

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  • @INPROCEEDINGS{10.4108/eai.7-6-2021.2308797,
        author={Kedri  Janardhana and P.  Rajasekar and A.  Shali and M.  Vijayaragavan and D. Stalin  David},
        title={Advanced Computer Network for Healthcare Sector -- Liver Function Diagnosis},
        proceedings={Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bharath University, Chennai, India},
        publisher={EAI},
        proceedings_a={I3CAC},
        year={2021},
        month={6},
        keywords={classifier big data precision accuracy algorithm recall},
        doi={10.4108/eai.7-6-2021.2308797}
    }
    
  • Kedri Janardhana
    P. Rajasekar
    A. Shali
    M. Vijayaragavan
    D. Stalin David
    Year: 2021
    Advanced Computer Network for Healthcare Sector – Liver Function Diagnosis
    I3CAC
    EAI
    DOI: 10.4108/eai.7-6-2021.2308797
Kedri Janardhana1,*, P. Rajasekar2, A. Shali3, M. Vijayaragavan4, D. Stalin David5
  • 1: Assistant Professor (Senior Grade), Department of Electrical Engineering, Faculty of Engineering, Dayalbagh Educational Institute (Deemed to be University), Agra, India
  • 2: Assistant Professor, Department of Information Technology, S.R.M Institute of Science and Technology
  • 3: Assistant Professor, Department of Computer Science and Engineering, Sri Sairam Engineering College, Chennai, India
  • 4: Assistant Professor, Department of Electrical and Electronics Engineering, Mailam Engineering College, Mailam, India
  • 5: Assistant Professor, Department of Computer Science and Engineering, IFET College of Engineering, Villupuram, India
*Contact email: janardhankedri@dei.ac.in

Abstract

Innovation technology has been implemented in different field, especially in life science, for example, genome sequencing, hereditary printing, proteomic and metabolomic examination, electronic clinical records, and patient-revealed wellbeing information have delivered a wide scope of information, from an assortment of people, cell types, and problems (huge information). Notwithstanding, this information should be incorporated and dissected in the event that it is to deliver models or ideas about physiological working or pathogenesis components. The majority of this information is freely accessible, which permits specialists anyplace to look for side effects of explicit biologic cycles or helpful targets for explicit infections or sorts of patients. We are evaluating ongoing advances in the field of computational and foundational science, and featuring the prospects of analysts utilizing enormous informational collections in the fields of gastroenterology and hepatology, to supplement conventional techniques for demonstrative and restorative access. We present and look at two AI calculations, which naturally create choice trees from research facility information. TheBayesNet classifier gives that the highest accuracy level which is 82.68%, and the highest precision value is 79.94% which is produced by NaivebayesMultinomial algorithm. This system recommends that the BayesNet classifier and N