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Liver Fibrosis Diagnosis Support System Using Machine Learning Methods

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Advanced Computing and Systems for Security

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 395))

Abstract

Liver fibrosis is a common disease of the European population (but not only them). It may have many backgrounds and may develop with a different rapidity—it may stay hidden for many years or rapidly develop into terminal stage called cirrhosis, where liver can no longer fulfill its function. Unfortunately, current methods of diagnosis are either connected with a potential risk for a patient and require a hospitalization or are expensive and not very accurate. This paper presents a comparative study of various feature selection algorithms combined with selected machine learning algorithms which may be used to build an advanced liver fibrosis diagnosis support system based on a nonexpensive and safe routine blood tests. Experiments carried out on a dataset collected by authors, proved usability and satisfactory accuracy of the presented algorithms.

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Acknowledgments

This work was supported by the Polish National Science Center under the grant no. DEC-2013/09/B/ST6/02264.

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Authors and Affiliations

  1. University of Silesia in Katowice, Institute of Computer Science, Bedzinska 39, Sosnowiec, Poland

    Tomasz Orczyk & Piotr Porwik

Authors
  1. Tomasz Orczyk
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  2. Piotr Porwik
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Corresponding author

Correspondence to Tomasz Orczyk .

Editor information

Editors and Affiliations

  1. A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, West Bengal, India

    Rituparna Chaki

  2. Università Ca' Foscari, Venice, Italy

    Agostino Cortesi

  3. Bialystok University of Technology Faculty of Computer Science, Bialystok, Poland

    Khalid Saeed

  4. University of Calcutta, Kolkata, West Bengal, India

    Nabendu Chaki

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Orczyk, T., Porwik, P. (2016). Liver Fibrosis Diagnosis Support System Using Machine Learning Methods. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 395. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2650-5_8

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  • DOI: https://doi.org/10.1007/978-81-322-2650-5_8

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2648-2

  • Online ISBN: 978-81-322-2650-5

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