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Prediction and Analysis of Liver Patient Data Using Linear Regression Technique

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Book cover Advances in Machine Learning and Data Science

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 705))

Abstract

In the current scenario, it is very difficult for the doctors to diagnose liver patient and there should be some kind of automated support based on machine intelligence that can help to diagnose in advance so that doctors start the treatment faster and save time. The machine intelligence is a way to predict the liver-related problems; in this study, the linear regression is used to predict the same, more accurately. The albumin levels are highly related in diagnosing these kinds of liver problems. The proposed model worked efficiently on 583 observations provided as well as on new datasets. The total average accuracy achieved in this proposed model was 89.34% which is much more than the previously identified research work of Wold et al. (SIAM J Sci Stat Comput, 5(3), 735–743, 1984, [1]) of 84.22%.

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Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Manipal University, Jaipur, India

    Deepankar Garg

  2. Department of Information Technology, Manipal University, Jaipur, India

    Akhilesh Kumar Sharma

Authors
  1. Deepankar Garg
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  2. Akhilesh Kumar Sharma
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Corresponding author

Correspondence to Deepankar Garg .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Goa, Goa, India

    Damodar Reddy Edla

  2. Department of Mathematics and Computing Science, Saint Mary’s University, Halifax, NS, Canada

    Pawan Lingras

  3. Department of Computer Science and Engineering, National Institute of Technology Goa, Goa, India

    Venkatanareshbabu K.

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Garg, D., Sharma, A.K. (2018). Prediction and Analysis of Liver Patient Data Using Linear Regression Technique. In: Reddy Edla, D., Lingras, P., Venkatanareshbabu K. (eds) Advances in Machine Learning and Data Science. Advances in Intelligent Systems and Computing, vol 705. Springer, Singapore. https://doi.org/10.1007/978-981-10-8569-7_8

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  • DOI: https://doi.org/10.1007/978-981-10-8569-7_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8568-0

  • Online ISBN: 978-981-10-8569-7

  • eBook Packages: EngineeringEngineering (R0)

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